Robust spectral analysis of videocapsule images acquired from celiac disease patients

Celiac Disease Detection From Videocapsule Endoscopy Images Using Strip Principal Component Analysis

The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease.

Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors

Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.

Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis

A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is primarily based on histological examination of biopsies. Recently, considerable effort has been undertaken to make use of image material by developing semi- or fully-automated systems to improve the diagnostic workup. Recently, focus was especially laid on developing state-of-the-art deep learning architectures, exploiting the endoscopist's expert knowledge and on making systems fully automated and thereby completely observer independent. In this work, we summarize recent trends in the field of computer-aided celiac disease diagnosis based on upper endoscopy and discuss about recent progress, remaining challenges, limitations currently prohibiting a deployment in clinical practice and future efforts to tackle them.

Coeliac disease and the videocapsule: what have we learned till now

Celiac disease is diagnosed in part by finding areas of pathology in the small bowel (SB) mucosa. This can often be difficult because the pathologic alterations, including atrophy of the small intestinal villi, can often be sparse and subtle. Some of the quantitative methods for detecting and measuring the presence of villous atrophy from videocapsule endoscopy (VCE) images are presented and discussed. These methods consist of static features of measurement including texture, gray level, and presence and abundance of fissures contained within each acquired image. The methods also consist of dynamic measurements including spectral analysis, and determining motion from a sequence of endoscopic images as obtained from a VCE clip. Thus far, several methods have been found useful to characterize the SB mucosa of untreated celiac disease patients versus control patients lacking villous atrophy, which have revealed significant differences in texture, frequency, and motion on analysis of VCE. In untreated celiac patients undergoing endoscopy, there tends to be greater magnitude of changes and spatial differences in textural descriptors, longer periodic components, indicating slower periodic activity, and differences in feature location, suggesting alterations in motility at areas of pathology as compared to patients without villous atrophy. Improvements in the quantitative analysis of VCE imaging in celiac patients is important to detect pathology in suspected patients, so that biopsies can be obtained from pertinent regions of the small intestinal mucosa. Improvements are also necessary so that patients with celiac disease can be monitored to evaluate the progress of mucosal healing after onset of treatment.

Extraction and processing of videocapsule data to detect and measure the presence of villous atrophy in celiac disease patients

BACKGROUND

Videocapsule endoscopy is a relative new method to analyze the gastrointestinal tract for the presence of pathologic features. It is of relevance to detect villous atrophy in the small bowel, which is a defining symptom of celiac disease.

METHOD

In this tutorial, methods to extract and process videocapsule endoscopy data are elucidated. The algorithms, computer code, and paradigms to analyze image series are described in detail. The topics covered include extraction of data, analysis of texture, eigenanalysis, spectral analysis, three-dimensional projection, and estimation of motility. The basic paradigms to implement these processes are provided.

RESULTS

Examples of successful quantitative analysis implementations for selected untreated celiac disease patients with villous atrophy versus control patients with normal villi were illustrated. Based on the implementations, it was evident that celiac patients tended to have a rougher small intestinal texture as compared with control patients. From three-dimensional projection, celiac patients exhibited larger surface protrusions emanating from the small intestinal mucosa, which may represent clumps of atrophied villi. The periodicity of small intestinal contractions tends to be slower when villous atrophy is present, and the estimated degree of motility is reduced as compared with control image series. Basis image construction suggested that fissuring and mottling of the mucosal surface is predominant in untreated celiac patients, and mostly absent in controls.

CONCLUSIONS

Implementation of computerized methods, as described in this tutorial, will likely be useful for the automated detection and measurement of villous atrophy, and to map its extent along the small intestine of celiac patients.

Overview of technical solutions and assessment of clinical usefulness of capsule endoscopy

The paper presents an overview of endoscopic capsules with particular emphasis on technical aspects. It indicates common problems in capsule endoscopy such as: (1) limited wireless communication (2) the use of capsule endoscopy in the case of partial patency of the gastrointestinal tract, (3) limited imaging area, (4) external capsule control limitations. It also presents the prospects of capsule endoscopy, the most recent technical solutions for biopsy and the mobility of the capsule in the gastrointestinal tract. The paper shows the possibilities of increasing clinical usefulness of capsule endoscopy resulting from technological limitations. Attention has also been paid to the current role of capsule endoscopy in screening tests and the limitations of its effectiveness. The paper includes the author's recommendations concerning the direction of further research and the possibility of enhancing the scope of capsule endoscopy.

Overview of technical solutions and assessment of clinical usefulness of capsule endoscopy

The paper presents an overview of endoscopic capsules with particular emphasis on technical aspects. It indicates common problems in capsule endoscopy such as: (1) limited wireless communication (2) the use of capsule endoscopy in the case of partial patency of the gastrointestinal tract, (3) limited imaging area, (4) external capsule control limitations. It also presents the prospects of capsule endoscopy, the most recent technical solutions for biopsy and the mobility of the capsule in the gastrointestinal tract. The paper shows the possibilities of increasing clinical usefulness of capsule endoscopy resulting from technological limitations. Attention has also been paid to the current role of capsule endoscopy in screening tests and the limitations of its effectiveness. The paper includes the author's recommendations concerning the direction of further research and the possibility of enhancing the scope of capsule endoscopy.

Quantitative measurements in capsule endoscopy

This review summarizes several approaches for quantitative measurement in capsule endoscopy. Video capsule endoscopy (VCE) typically provides wireless imaging of small bowel. Currently, a variety of quantitative measurements are implemented in commercially available hardware/software. The majority is proprietary and hence undisclosed algorithms. Measurement of amount of luminal contamination allows calculating scores from whole VCE studies. Other scores express the severity of small bowel lesions in Crohn׳s disease or the degree of villous atrophy in celiac disease. Image processing with numerous algorithms of textural and color feature extraction is further in the research focuses for automated image analysis. These tools aim to select single images with relevant lesions as blood, ulcers, polyps and tumors or to omit images showing only luminal contamination. Analysis of motility pattern, size measurement and determination of capsule localization are additional topics. Non-visual wireless capsules transmitting data acquired with specific sensors from the gastrointestinal (GI) tract are available for clinical routine. This includes pH measurement in the esophagus for the diagnosis of acid gastro-esophageal reflux. A wireless motility capsule provides GI motility analysis on the basis of pH, pressure, and temperature measurement. Electromagnetically tracking of another motility capsule allows visualization of motility. However, measurement of substances by GI capsules is of great interest but still at an early stage of development.

Quantitative image analysis of celiac disease

We outline the use of quantitative techniques that are currently used for analysis of celiac disease. Image processing techniques can be useful to statistically analyze the pixular data of endoscopic images that is acquired with standard or videocapsule endoscopy. It is shown how current techniques have evolved to become more useful for gastroenterologists who seek to understand celiac disease and to screen for it in suspected patients. New directions for focus in the development of methodology for diagnosis and treatment of this disease are suggested. It is evident that there are yet broad areas where there is potential to expand the use of quantitative techniques for improved analysis in suspected or known celiac disease patients.

Quantitative estimates of motility from videocapsule endoscopy are useful to discern celiac patients from controls

BACKGROUND

Prior work has shown that videocapsule endoscopy image features are a useful tool for quantitatively distinguishing the intestinal mucosal surface of untreated celiac patients from that of controls. The use of dynamic estimates of wall motility may further help to improve classification.

METHODS

Videocapsule endoscopy clips (200 frames each, 2 frames/s, 576 × 576 pixels/frame) were acquired at five small intestinal locations in 11 untreated celiac patients (celiacs) and ten controls. Color images were converted to grayscale and analyzed frame-by-frame. Variations in the position and width of the center of the small intestinal lumen were quantitatively estimated. The darkest grayscale pixels were used as an estimate of the lumen center. Over 200 frames, the standard deviation of the lumen center xy position and the mean and standard deviation in lumen center width were used as dynamic estimates of wall motility. These parameters were plotted in three-dimensional space, and the best discriminant function was used to classify celiacs versus controls at each of the following five locations: (1) duodenal bulb, (2) distal duodenum, (3) jejunum, (4) ileum, and (5) distal ileum.

RESULTS

The overall sensitivity for the classification of celiacs versus controls at all five locations was 98.2 %, while the specificity was 96.0 %. From location 1 to 5, there was a tendency for the lumen center width to diminish in terms of frame-to-frame variability by 7.6 % in celiacs (r (2) = 0.4) and 9.7 % in controls (r (2) = 0.7).

CONCLUSIONS

In addition to examining the mucosal surface, videocapsule endoscopy can assess small bowel intestinal motility and aid in distinguishing celiac patients from controls.

Quantitative estimates of motility from videocapsule endoscopy are useful to discern celiac patients from controls

BACKGROUND

Prior work has shown that videocapsule endoscopy image features are a useful tool for quantitatively distinguishing the intestinal mucosal surface of untreated celiac patients from that of controls. The use of dynamic estimates of wall motility may further help to improve classification.

METHODS

Videocapsule endoscopy clips (200 frames each, 2 frames/s, 576 × 576 pixels/frame) were acquired at five small intestinal locations in 11 untreated celiac patients (celiacs) and ten controls. Color images were converted to grayscale and analyzed frame-by-frame. Variations in the position and width of the center of the small intestinal lumen were quantitatively estimated. The darkest grayscale pixels were used as an estimate of the lumen center. Over 200 frames, the standard deviation of the lumen center xy position and the mean and standard deviation in lumen center width were used as dynamic estimates of wall motility. These parameters were plotted in three-dimensional space, and the best discriminant function was used to classify celiacs versus controls at each of the following five locations: (1) duodenal bulb, (2) distal duodenum, (3) jejunum, (4) ileum, and (5) distal ileum.

RESULTS

The overall sensitivity for the classification of celiacs versus controls at all five locations was 98.2 %, while the specificity was 96.0 %. From location 1 to 5, there was a tendency for the lumen center width to diminish in terms of frame-to-frame variability by 7.6 % in celiacs (r (2) = 0.4) and 9.7 % in controls (r (2) = 0.7).

CONCLUSIONS

In addition to examining the mucosal surface, videocapsule endoscopy can assess small bowel intestinal motility and aid in distinguishing celiac patients from controls.

Video capsule endoscopy in celiac disease

Video capsule endoscopy (VCE) provides a safe, non-invasive way to visualize the small intestine and is helpful in celiac disease patients in select situations. VCE can be performed in patients who are unable or unwilling to undergo conventional endoscopy, those with positive celiac serology with normal duodenal biopsies, and also in those who develop alarm symptoms. VCE has limitations including subjective interpretation. Techniques are being developed to standardize assessment of VCE images in patients with known or suspected celiac disease. Pilot studies using computer-based quantification methods have shown promise in examining the 3-dimensional mucosal structure and motility.

Improved frequency resolution for characterization of complex fractionated atrial electrograms

BACKGROUND

The dominant frequency of the Fourier power spectrum is useful to analyze complex fractionated atrial electrograms (CFAE), but spectral resolution is limited and uniform from DC to the Nyquist frequency. Herein the spectral resolution of a recently described and relatively new spectral estimation technique is compared to the Fourier radix-2 implementation.

METHODS

In 10 paroxysmal and 10 persistent atrial fibrillation patients, 216 CFAE were acquired from the pulmonary vein ostia and left atrial free wall (977 Hz sampling rate, 8192 sample points, 8.4 s duration). With these parameter values, in the physiologic range of 3-10 Hz, two frequency components can theoretically be resolved at 0.24 Hz using Fourier analysis and at 0.10 Hz on average using the new technique. For testing, two closely-spaced periodic components were synthesized from two different CFAE recordings, and combined with two other CFAE recordings magnified 2×, that served as interference signals. The ability to resolve synthesized frequency components in the range 3-4 Hz, 4-5 Hz, …, 9-10 Hz was determined for 15 trials each (105 total).

RESULTS

With the added interference, frequency resolution averaged 0.29 ± 0.22 Hz for Fourier versus 0.16 ± 0.10 Hz for the new method (p < 0.001). The misalignment error of spectral peaks versus actual values was ±0.023 Hz for Fourier and ±0.009 Hz for the new method (p < 0.001). One or both synthesized peaks were lost in the noise floor 13/105 times using Fourier versus 4/105 times using the new method.

CONCLUSIONS

Within the physiologically relevant frequency range for characterization of CFAE, the new method has approximately twice the spectral resolution of Fourier analysis, there is less error in estimating frequencies, and peaks appear more readily above the noise floor. Theoretically, when interference is not present, to resolve frequency components separated by 0.10 Hz using Fourier analysis would require an 18.2 s sequence duration, versus 8.4 s with the new method.

Identification of recurring patterns in fractionated atrial electrograms using new transform coefficients

Background

Identification of recurrent patterns in complex fractionated atrial electrograms (CFAE) has been used to differentiate paroxysmal from persistent atrial fibrillation (AF). Detection of the atrial CFAE patterns might therefore be assistive in guiding radiofrequency catheter ablation to drivers of the arrhythmia. In this study a technique for robust detection and classification of recurrent CFAE patterns is described.

Method

CFAE were obtained from the four pulmonary vein ostia, and from the anterior and posterior left atrium, in 10 patients with paroxysmal AF and 10 patients with longstanding persistent AF (216 recordings in total). Sequences 8.4 s in length were analyzed (8,192 sample points, 977 Hz sampling). Among the 216 sequences, two recurrent patterns A and B were substituted for 4 and 5 of the sequences, respectively. To this data, random interference, and random interference + noise were separately added. Basis vectors were constructed using a new transform that is derived from ensemble averaging. Patterns A and B were then detected and classified using a threshold level of Euclidean distance between spectral signatures as constructed with transform coefficients.

Results

In the presence of interference, sensitivity to detect and distinguish two patterns A and B was 96.2%, while specificity to exclude nonpatterns was 98.0%. In the presence of interference + noise, sensitivity was 89.1% while specificity was 97.0%.

Conclusions

Transform coefficients computed from ensemble averages can be used to succinctly quantify synchronized patterns present in AF data. The technique is useful to automatically detect recurrent patterns in CFAE that are embedded in interference without user bias. This quantitation can be implemented in real-time to map the AF substrate prior to and during catheter ablation.