Vasileios Charisis

Capsule endoscopy image analysis using texture information from various colour models

Wireless capsule endoscopy (WCE) is a novel imaging technique that is gradually gaining ground as it enables the non-invasive and efficacious visualization of the digestive track, and especially the entire small bowel including its middle part. However, the task of reviewing the vast amount of images produced by a WCE examination is a burden for the physicians. To tackle this major drawback, an innovative scheme for discriminating endoscopic images related to one of the most common intestinal diseases, ulceration, is presented here. This new approach focuses on colour-texture features in order to investigate how the structure information of healthy and abnormal tissue is distributed on RGB, HSV and CIE Lab colour spaces. The WCE images are pre-processed using bidimensional ensemble empirical mode decomposition so as to facilitate differential lacunarity analysis to extract the texture patterns of normal and ulcerous regions. Experimental results demonstrated promising classification performance (mean accuracy>95%), exhibiting a high potential towards automatic WCE image analysis.

Abnormal pattern detection in Wireless Capsule Endoscopy images using nonlinear analysis in RGB color space

In recent years, an innovative method has been developed for the non-invasive observation of the gastrointestinal tract (GT), namely Wireless Capsule Endoscopy (WCE). WCE especially enables a detailed inspection of the entire small bowel and identification of its clinical lesions. However, the foremost disadvantage of this technological breakthrough is the time consuming task of reviewing the vast amount of images produced. To address this, a novel technique for distinguishing pathogenic endoscopic images related to ulcer, the most common disease of GT, is presented here. Towards this direction, the Bidimensional Ensemble Empirical Mode Decomposition was applied to RGB color images of the small bowel acquired by a WCE system in order to extract their Intrinsic Mode Functions (IMFs). The IMFs reveal differences in structure from their finest to their coarsest scale, providing a new analysis domain. Additionally, lacunarity analysis was employed as a method to quantify and extract the texture patterns of the ulcer regions and the normal mucosa, respectively, in order to discriminate the abnormal from the normal images. Experimental results demonstrated promising classification accuracy (>95%), exhibiting a high potential towards WCE-based analysis.

Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: An educational tool to physicians

Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patients digestive tract and, especially, small intestine. However, reviewing the endoscopic data is time consuming and requires intense labor of highly experienced physicians. These limitations were the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are color-rotated in order to boost the chromatic attributes of ulcer regions. Then, texture information is extracted by utilizing the local binary pattern operator that analyses the spatial structure of the images at a very local level. Experimental results demonstrated promising classification accuracy (91.1%) exhibiting high potential towards a complete computer-aided diagnosis system that will not only reduce WCE data reviewing time, but also serve as an assisting tool for the training of inexperienced physicians.

A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images

Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patients digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.

Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis

The five senses constitute some of the most substantial elements of the human nature. Beyond their importance in daily life and perception of the world, they play crucial role in knowledge acquisition as well. For instance, medicine was one of the first domains where the conceptual tools of rationality and empiricism were combined with techniques of investigation to make the human body an object of knowledge (Foucault, 1973). In this context, the techniques mentioned above are based on the application of senses in order to acquire medical knowledge. More precisely, vision and hearing became specific objects of knowledge over the course of the 19th century, supplemented through technique and technology. Thus, seeing and hearing are to be understood as fundamentally and absolutely different modes of not only knowing the world, but also reaching a medical diagnosis.

Ulcer Detection in Wireless Capsule Endoscopy Images Using Bidimensional Nonlinear Analysis

Wireless Capsule Endoscopy (WCE) constitutes a recent technological breakthrough that enables the observation of the gastrointestinal tract (GT) and especially the entire small bowel in a non-invasive way compared to the traditional imaging techniques. WCE allows a detailed inspection of the intestine and identification of its clinical lesions. However, the main drawback of this method is the time consuming task of reviewing the vast amount of images produced. To address this, a novel technique for discriminating abnormal endoscopic images related to ulcer, the most common disease of GT, is presented here. Towards this direction, the Bidimensional Ensemble Empirical Mode Decomposition (BEEMD) was applied to images of the small bowel acquired by a WCE system in order to extract their Intrinsic Mode Functions (IMFs). The IMFs reveal differences in structure from their finest to their coarsest scale providing a new analysis domain. Additionally, lacunarity analysis was employed as a method to quantify and extract the texture patterns of the images and differentiate the ulcer from the healthy regions. Experimental results demonstrated promising classification accuracy (>90%), exhibiting a high potential towards WCE analysis.

A Multimodal Approach for the Safeguarding and Transmission of Intangible Cultural Heritage: The Case of i-Treasures

Intangible Cultural Heritage (ICH) creations include, amongst other, music, dance, singing, theatre, human skills, and craftsmanship. These cultural expressions are usually transmitted orally and/or using gestures and are modified over a period of time, through a process of collective recreation. As the world becomes more interconnected and many different cultures come into contact, local communities run the risk of losing important elements of their ICH, while young people find it difficult to maintain the connection with the cultural heritage treasured by their elders. In this paper, we present a novel holistic approach for the safeguarding and transmission of ICH that goes beyond the mere digitization of ICH content. Based on multisensory technology for the capturing of ICH, the proposed approach enables the generation of completely novel cultural content. High-level semantics are extracted from the acquired data, enabling researchers to identify possible implicit or hidden correlations between different ICH expressions or interpretation styles and study the evolution of a specific ICH. These data, coupled with other cultural resources, are accessible through the i-Treasures Web-platform, which provides the means for supporting knowledge exchange between researchers as well as know-how transmission from ICH bearers to apprentices.

EmoActivity - An EEG-Based Gamified Emotion HCI for Augmented Artistic Expression: The i-Treasures Paradigm

There are important cultural differences in emotions that can be predicted and connected to each other in the light of cultural and artistic expressions. The main differences reflected at the affective space are expressed through initial response tendencies of appraisal and action readiness. Capturing and handling the emotions during artistic activities could be used as a dominant source of information to acquire and augment the cultural expression and maximize the emotional impact to the audience. This paper presents a novel EEG-based game-like application, to learn and handle affective states and transitions towards augmented artistic expression. According to the game scenario, the user has to reach and sustain one or more target affective states based on the level of the game, the difficulty setting and his/her current affective state. The game, although at its first version, has been demonstrated to a small group of potential users and has received positive feedback. Its use by a wider audience is anticipated within the realization of the i-Treasure FP7 EU Programme (2013-2017).

Intrinsic higher-order correlation and lacunarity analysis for WCE-based ulcer classification

Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patients digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data is a burden for the physicians. This limitation was the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.

The i-Treasures Intangible Cultural Heritage dataset

In this paper, we introduce the i-Treasures Intangible Cultural Heritage (ICH) dataset, a freely available collection of multimodal data captured from different forms of rare ICH. More specifically, the dataset contains video, audio, depth, motion capture data and other modalities, such as EEG or ultrasound data. It also includes (manual) annotations of data, while in some cases additional features and metadata are provided, extracted using algorithms and modules developed within the i-Treasures project. We describe the creation process (sensors, capture setups and modules used), the dataset content and the associated annotations. An attractive feature of this ICH Database is that its the first of its kind, providing annotated multimodal data for a wide range of rare ICH types. Finally, some conclusions are drawn and the future development of the dataset is discussed.