**1. Introduction**

184 New Advances in the Basic and Clinical Gastroenterology

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Richter, J. E. (1989). Hypertensive lower esophageal sphincter: what does it mean? *Journal of Clinical Gastroenterology*, Vol. 11, No. 2, (April 1989), pp. 139-146, ISSN 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.

A branch of medicine closely associated with one of these techniques, namely visual inspection, is gastroenterology. In the field of gastroenterology, vision is widely understood as the fundamental mode of knowing the state of the gastrointestinal (GI) tract. The advent of medical imaging technologies, such as radiography (in the wider sense), tomography and especially endoscopy, promoted this thesis (Lorenz *et al.*, 1993; Rutgeerts *et al.*, 1980) by enabling the visual examination without demanding to gain physical access. In case of visual inspection, as in case of auscultation, there are specific properties observed in order to assess the image content, no matter how simple or sophisticated the imaging technology is. During a stethoscope examination, for instance, the clinician attempts to identify frequency, pitch and duration deviations from the normal lung sounds. Similarly, during the observation of a medical image, there are image properties, corresponding to the acoustic ones of the pulmonary system, which may reveal the existence of illness. In the case of endoscopic GI tract images, these features essentially include texture, color and shape. The procedure that a clinician subconsciously follows in order to examine the images and reach a diagnosis is to seek for distortions. Distortions mainly in texture and color of the examined tissue, as compared to the features considered empirically or conceptually healthy. While color and shape are quite tangible approaches, the concept of texture is more abstract and subjectively defined and interpreted; however, embodies valuable information that can be used to identify or describe an image (Haralick *et al.*, 1973). The vagueness of this concept is evidenced by the fact that there is no universally agreed-upon definition of what image texture is and, in general, different researchers use different definitions depending upon the

Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis 187

The aim of WCE image processing techniques is to help the physician draw more reliable conclusions by generating enhanced and more informative images. During an examination with a stethoscope the physician instructs the patient how to breathe in order to get the best possible auscultation. In a similar way, a gastroenterologist who reviews an endoscopic video would desire the images to be as much informative as possible, but, without the opportunity to either give instructions to the patient or guide the capsule. The automated intestinal-disorder recognition systems target to detect potential regions of abnormal tissue in order to help the physician reach a diagnosis more quickly. This is particularly essential in WCE due to the vast amount of images produced (over 55.000) and the highly timeconsuming task of reviewing them (more than two hours) (Maieron *et al.*, 2004). The fact that abnormal findings might not be clearly apparent to the naked eye further renders computeraided image analysis imperative. It is not rare that abnormal findings are visible in only one or two WCE frames and easily missed by the physician. Additionally, contrast between malignant and normal tissue may be present but below the threshold of human perception. The human visual system fails to detect certain textured patterns. Julesz, an experimental psychologist, was an early pioneer in the visual perception of texture. He verified that human eye can discriminate textures that differ up to second order statistics (Julesz, 1975). In other words, a deliberate amount of effort is required to discriminate between two textures with identical second order statistics. Last but not least, the young age of WCE implies that most clinicians are inexperienced in this examination and automatic diagnostic systems could be great tools for those who wish to become experts in WCE. In this context,

automatic inspection and analysis of WCE images is of immediate need.

However, their success rate is limited.

From the aforementioned perspectives, it becomes clear that the auxiliary automatic diagnostic systems should exploit the texture and color features of the endoscopic images. Many efforts and computational approaches towards WCE image analysis have been reported in the literature. More specifically, researchers are concerned with malicious tissue detection which refers to detection of abnormal regions, such as tumors, polyps, bleeding and ulcer. To cope with this matter, traditional pattern recognition methods are applied, utilizing both chromatic and achromatic image domains. In particular, detection of abnormal patterns is achieved by employing local color features (Li & Meng, 2007), texture unit number (NTU) transformation and texture spectrum (Kodogiannis *et al.*, 2007a, 2007b) and synergistic methodologies, such as L-G graphs and image registration (Bourbakis, 2005). Local binary patterns (Iakovidis *et al.*, 2006) with the aid of G-statistic (Wang *et al.*, 2006), cooccurrence matrices (Ameling *et al.*, 2009), and discrete wavelet transform in conjunction with second-order statistics (Karkanis *et al.*, 2007; Magoulas, 2006) contributed to polyp and tumor detection. Regarding ulcer recognition, the related research is quite limited, no matter how common and important this disease is. The techniques proposed include feature extraction from a curvelet-based uniform local binary pattern (Iakovidis *et al.*, 2006; Li & Meng, 2009b), chromaticity moments calculated with the aid of Chebychev polynomials (Li & Meng, 2009a), texture spectrum (Kodogiannis *et al.*, 2007b), MPEG-7 descriptors (Coimbra & Cunha, 2006) and Red-Green-Blue (RGB) pixel values evaluation (Gan *et al.*, 2008).

This chapter sheds light upon one of the main issues of the WCE image analysis field, i.e., the overall detection enhancement of one of the most common diseases in the GI tract, namely ulcer; hence, enriching the inadequate existing literature. This goal is achieved by emphasizing on efficient elicitation of the structure characteristics of ulcerations and by

particular area of application (Tuceryan & Jain, 1998). The most widely used and accepted definition of texture in the field of medical image analysis, which is also adopted in this chapter, is the one that defines texture as the spatial variation of pixel intensities. In other words, texture describes the relationship between the intensities of neighbouring pixels (not necessarily adjacent). Texture is a fundamental characteristic that entails substantial information about the structural arrangement of surfaces and their relationship to the surrounding environment. This information may be applied to estimate shape, surface orientation, depth changes and construction materials. Texture is an innate property of virtually all surfaces; the grain of the wood, the weave of a fabric, the pattern of crops in a field, rugae on the mucous membrane of the stomach, the mucosa of colon and small intestine. An example of various texture patterns is given in Fig. 1. This structural information has been proven crucial for medical image analysis and interpretation (Miller & Astley, 1992; Xie *et al.*, 2005). This is the case especially for gastroenterology where the internal mucous membranes of the digestive tract exhibit strong textural features and distinctive patterns. For instance, an eroded ulcerous region or a protruded cancerous tissue is visually distinguished, mainly, by its alternated texture. Another image property also important for abnormal tissue evaluation is color. Ulcers, for instance, exhibit greenish-yellow hues while an active bleeding spot is characterized by deep red tones. On the contrary, the normal intestinal mucous membrane is reddish-pink in color. Nevertheless, color cannot be utilized as a standalone objective modality for abnormal tissue detection, since it is not perceptually uniform. The perceived color is highly conditioned by the nature and the amount of ambient luminosity (Berlin & Kay, 1969). Despite the color constancy effect (Foster *et al.*, 1997) of the human color perception system, whereby the color perception of objects remains relatively constant under varying environmental and visual conditions, serious color variations and color casts exist because of the intervention of an endoscope or a camera between the intestinal tissue and the physician's eye. For these reasons, gastroenterologists use color and texture information together, as the visual clues, along with other complementary examinations (i.e., blood tests, urine tests etc.), in order to reach a diagnosis.

Fig. 1. Digital images with visibly different texture regions: a) grain of wood, b) water, c) cloth, d) corn crops, e) forest, f) grass, g) stomach, h) colon, i) esophagus.

The advent of Wireless Capsule Endoscopy (WCE) and the gastroenterologists' requirement for faster and more secure diagnoses necessitated the development of effective intestinaldisorder recognition systems and automated WCE image analysis/inspection techniques.

particular area of application (Tuceryan & Jain, 1998). The most widely used and accepted definition of texture in the field of medical image analysis, which is also adopted in this chapter, is the one that defines texture as the spatial variation of pixel intensities. In other words, texture describes the relationship between the intensities of neighbouring pixels (not necessarily adjacent). Texture is a fundamental characteristic that entails substantial information about the structural arrangement of surfaces and their relationship to the surrounding environment. This information may be applied to estimate shape, surface orientation, depth changes and construction materials. Texture is an innate property of virtually all surfaces; the grain of the wood, the weave of a fabric, the pattern of crops in a field, rugae on the mucous membrane of the stomach, the mucosa of colon and small intestine. An example of various texture patterns is given in Fig. 1. This structural information has been proven crucial for medical image analysis and interpretation (Miller & Astley, 1992; Xie *et al.*, 2005). This is the case especially for gastroenterology where the internal mucous membranes of the digestive tract exhibit strong textural features and distinctive patterns. For instance, an eroded ulcerous region or a protruded cancerous tissue is visually distinguished, mainly, by its alternated texture. Another image property also important for abnormal tissue evaluation is color. Ulcers, for instance, exhibit greenish-yellow hues while an active bleeding spot is characterized by deep red tones. On the contrary, the normal intestinal mucous membrane is reddish-pink in color. Nevertheless, color cannot be utilized as a standalone objective modality for abnormal tissue detection, since it is not perceptually uniform. The perceived color is highly conditioned by the nature and the amount of ambient luminosity (Berlin & Kay, 1969). Despite the color constancy effect (Foster *et al.*, 1997) of the human color perception system, whereby the color perception of objects remains relatively constant under varying environmental and visual conditions, serious color variations and color casts exist because of the intervention of an endoscope or a camera between the intestinal tissue and the physician's eye. For these reasons, gastroenterologists use color and texture information together, as the visual clues, along with other complementary examinations (i.e., blood tests, urine tests etc.), in

Fig. 1. Digital images with visibly different texture regions: a) grain of wood, b) water, c)

The advent of Wireless Capsule Endoscopy (WCE) and the gastroenterologists' requirement for faster and more secure diagnoses necessitated the development of effective intestinaldisorder recognition systems and automated WCE image analysis/inspection techniques.

cloth, d) corn crops, e) forest, f) grass, g) stomach, h) colon, i) esophagus.

order to reach a diagnosis.

The aim of WCE image processing techniques is to help the physician draw more reliable conclusions by generating enhanced and more informative images. During an examination with a stethoscope the physician instructs the patient how to breathe in order to get the best possible auscultation. In a similar way, a gastroenterologist who reviews an endoscopic video would desire the images to be as much informative as possible, but, without the opportunity to either give instructions to the patient or guide the capsule. The automated intestinal-disorder recognition systems target to detect potential regions of abnormal tissue in order to help the physician reach a diagnosis more quickly. This is particularly essential in WCE due to the vast amount of images produced (over 55.000) and the highly timeconsuming task of reviewing them (more than two hours) (Maieron *et al.*, 2004). The fact that abnormal findings might not be clearly apparent to the naked eye further renders computeraided image analysis imperative. It is not rare that abnormal findings are visible in only one or two WCE frames and easily missed by the physician. Additionally, contrast between malignant and normal tissue may be present but below the threshold of human perception. The human visual system fails to detect certain textured patterns. Julesz, an experimental psychologist, was an early pioneer in the visual perception of texture. He verified that human eye can discriminate textures that differ up to second order statistics (Julesz, 1975). In other words, a deliberate amount of effort is required to discriminate between two textures with identical second order statistics. Last but not least, the young age of WCE implies that most clinicians are inexperienced in this examination and automatic diagnostic systems could be great tools for those who wish to become experts in WCE. In this context, automatic inspection and analysis of WCE images is of immediate need.

From the aforementioned perspectives, it becomes clear that the auxiliary automatic diagnostic systems should exploit the texture and color features of the endoscopic images. Many efforts and computational approaches towards WCE image analysis have been reported in the literature. More specifically, researchers are concerned with malicious tissue detection which refers to detection of abnormal regions, such as tumors, polyps, bleeding and ulcer. To cope with this matter, traditional pattern recognition methods are applied, utilizing both chromatic and achromatic image domains. In particular, detection of abnormal patterns is achieved by employing local color features (Li & Meng, 2007), texture unit number (NTU) transformation and texture spectrum (Kodogiannis *et al.*, 2007a, 2007b) and synergistic methodologies, such as L-G graphs and image registration (Bourbakis, 2005). Local binary patterns (Iakovidis *et al.*, 2006) with the aid of G-statistic (Wang *et al.*, 2006), cooccurrence matrices (Ameling *et al.*, 2009), and discrete wavelet transform in conjunction with second-order statistics (Karkanis *et al.*, 2007; Magoulas, 2006) contributed to polyp and tumor detection. Regarding ulcer recognition, the related research is quite limited, no matter how common and important this disease is. The techniques proposed include feature extraction from a curvelet-based uniform local binary pattern (Iakovidis *et al.*, 2006; Li & Meng, 2009b), chromaticity moments calculated with the aid of Chebychev polynomials (Li & Meng, 2009a), texture spectrum (Kodogiannis *et al.*, 2007b), MPEG-7 descriptors (Coimbra & Cunha, 2006) and Red-Green-Blue (RGB) pixel values evaluation (Gan *et al.*, 2008). However, their success rate is limited.

This chapter sheds light upon one of the main issues of the WCE image analysis field, i.e., the overall detection enhancement of one of the most common diseases in the GI tract, namely ulcer; hence, enriching the inadequate existing literature. This goal is achieved by emphasizing on efficient elicitation of the structure characteristics of ulcerations and by

Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis 189

one of the most common lesions of the GI tract that affects approximately 10% of the people. The most usual causes are Helicobacter pylori bacteria and use of nonsteroidal antiinflammatory drugs (NSAID). Ulcer is a chronic inflammatory sore or erosion on the internal mucous membranes that arises in small intestine, especially in duodenum (the upper part of the small intestine) and in stomach. Some serious diseases are associated with ulcer, like Crohn's disease and ulcerative colitis. Although ulcer by itself is not lethal, complications are capable of causing death. That is the reason why early diagnosis and

WCE is a well-tolerated and safe procedure with very few and rare complications. The main risk of WCE is capsule retention. Despite the small diameter of the capsule, a narrowing of the small bowel may cause it to become retained at a site of stricture. However, retention is estimated to occur in less than 1% of cases (Liao *et al.,* 2010). The 2005 International Conference on Capsule Endoscopy reached a consensus stating that a capsule would be deemed to have been retained if it could be shown to be remaining in the GI tract more than two weeks, without symptoms, after it had been ingested. The causes of capsule retention are bowel obstructions narrower than the size of the capsule (11mm diameter). Small bowel strictures are a frequent complication of Crohn's disease (Cheifetz *et al.*, 2006) and prolonged use of NSAID (Meredith *et al.*, 2009). The risk of capsule retention is also high for patients with a history of bowel obstruction or a previous gastrointestinal surgery. In case of retention, the removal of the capsule is most commonly performed by surgery (Barkin & Friedman, 2002), often resecting the obstructing lesion at the same time. However, there are

In order to reduce the risk of retaining the capsule, a barium small bowel examination should be performed or a biodegradable patency capsule (Fig. 2) (Riccioni *et al.*, 2003) should be digested prior to WCE. However, two studies (Meredith *et al.*, 2009) indicated that small bowel follow through radiography (SBFT) investigations were not effective at excluding patients at risk of retention. Additionally, patients with abnormal SBFT can have successful WCE. The patency capsule is exactly the same size as the capsule endoscope but it is made from lactose, with 10% barium sulphate to make it radiopaque, and surrounded by

cases where the removal is possible with traditional endoscopic techniques.

Fig. 2. Schematic drawing of a biodegradable patency capsule.

treatment is extremely essential.

**2.1 Side effects** 

introducing new feature vectors (FVs). In particular, this chapter describes the use of innovative computer vision approaches towards the evaluation of ulcer-related content of WCE images. These approaches are similar to those employed by physicians in clinical practice to reach a diagnosis, i.e., the concept of color-texture characteristics. More specifically, they include sophisticated image processing tools with robust mathematical background, drawn from the field of Multi-Resolution Analysis, resulting in discrimination between ulcer and healthy regions. Additionally, innovative feature extraction algorithms, structured in both space and space-frequency domains, are presented, along with their application to real WCE data, collected from patients with ulcerous diseases. The chapter concludes by pointing out the potential of the proposed approaches towards efficient automated ulcer detection systems that will moderate the labour of the gastroenterologist and, consequently, the cost of the WCE examination.
