Dimitris K. Iakovidis

Computer-aided tumor detection in endoscopic video using color wavelet features

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

Software for enhanced video capsule endoscopy: challenges for essential progress

Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear—parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

Video-based measurements for wireless capsule endoscope tracking

The wireless capsule endoscope is a swallowable medical device equipped with a miniature camera enabling the visual examination of the gastrointestinal (GI) tract. It wirelessly transmits thousands of images to an external video recording system, while its location and orientation are being tracked approximately by external sensor arrays. In this paper we investigate a video-based approach to tracking the capsule endoscope without requiring any external equipment. The proposed method involves extraction of speeded up robust features from video frames, registration of consecutive frames based on the random sample consensus algorithm, and estimation of the displacement and rotation of interest points within these frames. The results obtained by the application of this method on wireless capsule endoscopy videos indicate its effectiveness and improved performance over the state of the art. The findings of this research pave the way for a cost-effective localization and travel distance measurement of capsule endoscopes in the GI tract, which could contribute in the planning of more accurate surgical interventions.

Wireless endoscopy in 2020: Will it still be a capsule?

Currently, the major problem of all existing commercial capsule devices is the lack of control of movement. In the future, with an interface application, the clinician will be able to stop and direct the device into points of interest for detailed inspection/diagnosis, and therapy delivery. This editorial presents current commercially-available new designs, European projects and delivery capsule and gives an overview of the progress required and progress that will be achieved -according to the opinion of the authors- in the next 5 year leading to 2020.

Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software

BACKGROUND The advent of wireless capsule endoscopy (WCE) has revolutionized the diagnostic approach to small-bowel disease. However, the task of reviewing WCE video sequences is laborious and time-consuming; software tools offering automated video analysis would enable a timelier and potentially a more accurate diagnosis. OBJECTIVE To assess the validity of innovative, automatic lesion-detection software in WCE. DESIGN/INTERVENTION A color feature-based pattern recognition methodology was devised and applied to the aforementioned image group. SETTING This study was performed at the Royal Infirmary of Edinburgh, United Kingdom, and the Technological Educational Institute of Central Greece, Lamia, Greece. MATERIALS A total of 137 deidentified WCE single images, 77 showing pathology and 60 normal images. RESULTS The proposed methodology, unlike state-of-the-art approaches, is capable of detecting several different types of lesions. The average performance, in terms of the area under the receiver-operating characteristic curve, reached 89.2 ± 0.9%. The best average performance was obtained for angiectasias (97.5 ± 2.4%) and nodular lymphangiectasias (96.3 ± 3.6%). LIMITATIONS Single expert for annotation of pathologies, single type of WCE model, use of single images instead of entire WCE videos. CONCLUSION A simple, yet effective, approach allowing automatic detection of all types of abnormalities in capsule endoscopy is presented. Based on color pattern recognition, it outperforms previous state-of-the-art approaches. Moreover, it is robust in the presence of luminal contents and is capable of detecting even very small lesions.

Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions

This review presents issues pertaining to lesion detection in small-bowel capsule endoscopy (SBCE). The use of prokinetics, chromoendoscopy, diagnostic yield indicators, localization issues and the use of 3D reconstruction are presented. The authors also review the current status (and future expectations) in automatic lesion detection software development. Automatic lesion detection and reporting, and development of an accurate lesion localization system are the main software challenges of our time. The ‘smart’, selective and judicious use (before as well as during SBCE) of prokinetics in combination with other modalities (such as real time and/or purge) improves the completion rate of SBCE. The tracking of the capsule within the body is important for the localization of abnormal findings and planning of further therapeutic interventions. Currently, localization is based on transit time. Recently proposed software and hardware solutions are proposed herein. Moreover, the feasibility of software-based 3D representation (attempt for 3D reconstruction) is examined.

Tumor recognition in endoscopic video images using artificial neural network architectures

The paper focuses on a scheme for automated tumor recognition using images acquired during endoscopic sessions. The proposed recognition system is based on multilayer feed forward neural networks (MFNNs) and uses texture information encoded with corresponding statistical measures that are fed as input to the MFNN. Experiments were performed for recognition of different types of tumors in various images and also a number of sequentially acquired frames. The recognition of a polypoid tumor of the colon in the original image, which were used for training was very high. The trained network was also able to satisfactorily recognize the tumor in a sequence of video frames. The results of the proposed approach were very promising and it seems that it can be efficiently applied for tumor recognition.

Unsupervised summarisation of capsule endoscopy video

Capsule endoscopy is a non-invasive imaging technique commonly used for screening of the entire small intestine. It is performed by a wireless swallowable endoscopic capsule capable of transmitting thousands of video frames per examination. The visual inspection of the vast amount of images acquired during such an examination is a subjective and highly time consuming task even for experienced gastroenterologists. In this paper we propose a novel approach to the reduction of the number of the video frames to be inspected so as to enable faster inspection of the endoscopic video. It is based on symmetric non-negative matrix factorisation initialised by the fuzzy c-means algorithm and it is supported by non-negative Lagrangian relaxation to extract a subset of video frames containing the most representative scenes from a whole endoscopic examination. The experimental evaluation of the proposed approach was tested on annotated endoscopic videos with frames displaying ulcers, bleedings and normal tissues from various sites in the small intestine. The results demonstrate that the video summary produced consists of representative frames from all the abnormal findings and the normal tissues of the input video.

Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding

Portable chest radiography is a valuable tool for screening patients hospitalized in intensive care, providing visual cues for diagnosis and physiological measurements. However, its practicality comes at the cost of quality, which is mainly affected by misaligned body positioning, thus increasing x-ray misinterpretation rates. This paper presents a novel methodology for the detection of the lung field boundaries in portable chest radiographs of patients with bacterial pulmonary infections. Such infections are radiographically manifested as foci of consolidations which can lead to vague or invisible lung field boundaries, difficult to distinguish even by experienced physicians. Conventional and state-of-the-art approaches address mainly stationary radiographs, whereas only a few of them cope with pulmonary infections. The proposed methodology is based on an active shape model incorporating shape prior information about the lung fields. The model is initialized by a novel technique utilizing a set of salient points detected on the peripheral anatomic structures of the lungs. A selective thresholding algorithm based on a spinal cord sampling process supports both the initialization and the evolution of the model for the detection of the lung field boundaries. The experiments show that the proposed methodology outperforms state-of-the-art approaches.

Evaluation of textural feature extraction schemes for neural network-based interpretation of regions in medical images

A few approaches have been presented in the literature towards the discrimination of texture in medical images. Medical experts proposed that the more valuable information for discriminating among normal and suspicious cancer regions in endoscopic images is the texture of the examined tissue. Texture can be encoded by a number of mathematical descriptors. Three well-known textural descriptors, as well as a new wavelet-based one are used in this paper for an accurate study and evaluation of the methodologies encountered. Experiments conducted include tests with various images from the Brodatz album, as well as interpretation of tissue regions in endoscopic image. In all cases the recognition task is supported by multilayer perceptron type neural network architectures.