**8. Overall perspective and future work**

204 New Advances in the Basic and Clinical Gastroenterology

The proposed AR-DLac scheme, thoroughly described in §5.3, selects the optimum IMFs. In the reconstruction scenario (R-case) the selected IMFs and the residue reconstruct a new image from which the FV is extracted, while in non reconstruction scenario (NR-case) the FVs from the selected IMFs are concatenated in order to form the final FV. The results of both scenarios are tabulated in Table 2, for all classifiers. The best classification rates for each implementation scenario are notated in bold. The format %±% corresponds to mean acc.,

> **Implementation scenario R-case NR-case**  Acc. Sens. Spec. Acc. Sens. Spec.

LDA 95.5±0.5 94.8±0.8 96.2±0.4 89.2±0.9 84.3±1.7 94.1±0.4 QDA 95.8±0.6 93.9±1.1 97.7±0.3 **91.2±0.9 86.2±1.3 95.9±1.1**  MD 96.3±0.2 96.0±0.1 96.6±0.6 78.7±1.4 98.3±0.9 58.3±2.7 SVM **96.7±0.6 96.5±0.3 96.9±0.9** 89.7±0.8 87.5±1.0 91.9±0.9

Table 2. Mean classification rates (%) for both R-case and NR-case scenarios, for all

The most efficient performance of the proposed AR-DLac scheme is delivered in R-case scenario where the classification accuracy reaches 96.7% by exploiting SVM classifier. However, the difference between the most (SVM) and the least (LDA) efficient classifier is only 1.2 percentage points, in terms of accuracy, implying that the features extracted by the proposed analysis are quite robust, exhibiting advanced overall performance regardless of the classification algorithm engaged. It is also remarkable the fact that the high accuracy rate is accompanied by high and relatively consistent rates for both sensitivity (96.5%) and specificity (96.9%). These results indicate that AR-DLac scheme is capable of equally recognising ulcer and normal data without exhibiting bias towards a specific pattern. This behaviour applies to all examined classifiers since the difference between sensitivity and

The classification results of the NR-case, suggest inferior performance of the AR-DLac scheme during the non reconstruction scenario. The most effective classifier for NR-case is QDA delivering 91.2% accuracy, 86.2% sensitivity and 95.9% specificity. These rates, compared to those of R-case, are 5.5, 10.3 and 1.0 percentage points lower, respectively. Even the worst scenario for R-case (LDA classifier) achieves 4.3 percentage points higher accuracy than QDA in NR-case. Moreover, the balance between sensitivity and specificity rates deteriorated ranging from 4.4 (for SVM) to 40 (for MD) percentage points. MD, as in the individual IMF case, fails to recognize correctly the normal images since the specificity rate is 58.3%. The divergence in performance between R-case and NR-case indicates that the recomposed image by the optimal IMFs and the residue represents more efficiently the intestinal texture information than the individual components of the image. This may be explained by the fact that in NR-case the trend of the image (residue) is ignored. Moreover, the fact that FVNR includes 3 IMFs x 8 features = 24 features in total (33% larger than FVR)

**7.2 Reconstruction (R-case) – Non Reconstruction (NR-case) scenarios** 

sens. and spec. ± standard deviation.

classifiers (LDA, QDA, MD, SVM).

specificity does not exceed 2.6 percentage points.

may also affect the classification performance.

**Classifier** 

The aforementioned experimental results highlight the potential of the proposed scheme towards ulcer and healthy intestinal tissue discrimination. The optimum image components (IMFs) that contain the majority of texture information include IMFs 5, 6 and 8. Individual IMFs score up to 84% classification accuracy, while their exploitation as a group enhances the detection rate up to 91.2%. On the contrary, the refined image reconstruction process achieves 96.7% successful tissue identification. When compared with other approaches, the proposed scheme seems to be more effective exhibiting increased classification ability by using a smaller feature vector. One of the most efficient ulcer detection approaches (Kodogiannis *et al.*, 2007b), used as a comparison baseline, employs 54 features (instead of 8) and results in 94.5% accuracy when applied to our dataset (Charisis *et al.*, 2011).

In spite of the promising performance of the proposed scheme, there are still some issues for improvements that should be taken under consideration for its use in a future computer aided diagnosis system. Firstly, the number of ulcer and normal images should be increased in order to secure maximum diversity between the ulcer cases, develop more robust algorithms and obtain more accurate conclusions. Secondly, automatic segmentation of the regions of interest (ROI) is mandatory. In our approach, WCE images are manually cropped to the ROI. A potential solution is to divide each WCE image (576x576) into small patches (64x64) and choose the patches that depict mucosa. These patches contain almost all possible ROI by covering the most valid area of the original WCE image. At last, the computational cost of the proposed techniques should be revised. In this work, the introduction of a real time application was not our objective. In this context, the computational cost of the current unoptimized MATLAB code is 9.3 seconds per ROI, considering an average size of 145x145 pixels. BEEMD analysis consumes 83.4% of this time while DLac analysis and classification absorb 16.2% and 0.4%, respectively. When focusing on real time application, dedicated hardware and programming languages, more efficient implementation algorithms (for BEEMD and DLac) and multithreading programming should be considered.

#### **9. Conclusion**

Wireless capsule endoscopy (WCE) is a novel, non-invasive form of endoscopy that has started a new era for the visual inspection of the entire small bowel. A WCE system consists of a pill-shaped, wireless capsule that the patient swallows. The capsule, propelled by the natural bowel movements, captures and transmits images from the internal mucous membranes, along its journey through the digestive tract. Despite the revolution WCE has introduced, there are several limitations that pose serious questions about the competency of WCE compared to probe gastroscopy and colonoscopy. Some of the major challenges include camera speed/quality, power supply, controllable manoeuvring and interventional capabilities. Significant research is conducted towards this direction, various approaches have been proposed and the first achievements have emerged. A new capsule equipped

Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis 207

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with two cameras and increased battery life has been developed for colon and esophagus examination. Magnetically remote controlling and wireless power transmission concepts are in experimental phase. Apart from the hardware-oriented limitations and approaches there is a major issue concerning the vast amount of data produced by a WCE examination. The optimal WCE needs to contain a computerized system for automatic detection of pathologies such as ulcer and polyps in order to overcome the drawback of time-consuming reviewing of the video (Fireman, 2010). In this context, researchers aim to develop automatic diagnosis systems. The main contribution of this work is the presentation of a novel tool for WCE image analysis and classification by exploiting color-texture features. This colortexture perspective was inspired by the one the gastroenterologist usually adopts in clinical practice for successful diagnosis of pathologies and, especially, ulcer. The proposed AR-DLac scheme is based on the ingenious combination of BEEMD and DLac, applied on the green component of WCE images in order to identify ulcerations. BEEMD, apart from an adaptive image denoising tool, was exploited to reveal the intrinsic components (IMFs) of the images in order to achieve data driven, adaptive image refinement (AR), boost the distinctness between normal and ulcer regions and facilitate DLac analysis to extract efficient texture characteristics. AR entailed optimum IMF selection, based on the structure patterns of IMFs disclosed by DLac. The optimum IMFs were used either to reconstruct a new, refined image, or provide separate images. The proposed AR-DLac approach was evaluated on selected WCE images, captured from patients, depicting ulcer and healthy tissue. Experimental results have shown that AR-DLac scheme exhibits quite satisfactory overall classification performance. Intestinal texture information is distributed along IMFs 3 to 8; thus, the utilization of a single IMF for the detection procedure is not recommended. The classification performance of individual IMFs does not exceed 84% (classification accuracy), with IMF5 being the most efficient. When individual images (i.e., optimum IMFs) are employed (non reconstruction case) the performance improves and accuracy reaches 91.2%. However, the best results are delivered in the reconstruction case, where accuracy, sensitivity and specificity exceed 96.5%. The advanced overall classification performance of AR-DLac approach paves the way for its use in a provisional automatic diagnosis system.

#### **10. References**


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**10** 

*Russian Federation* 

**Methods of Protein Digestive** 

Mikhail Akimov and Vladimir Bezuglov

**Stability Assay – State of the Art** 

*Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science* 

The evaluation of protein stability in digestive tract is an important topic in various fields of biomedical sciences. The most straightforward task within it is the evaluation of food quality, as during the food processing the digestibility of a protein could fall dramatically. Another task, tightly connected with the previous one, is the evaluation of protein allergenicity. This parameter is shown to be dependent on protein's digestibility, the less digestible ones having the highest probability to induce immune response. Finally, the task of development of new peptide drugs also requires digestive stability check for *per os* drug

Current knowledge on protein digestion suggests it to be a multistage process, starting from pepsin cleavage in stomach and proceeding through trypsin and chymotrypsin digestion in intestinal lumen, and finally involving cleavage by intestinal surface and intracellular proteases. The latter two protease groups accomplish the most deep protein degradation. An intriguing point is that in spite of this knowledge the models, which include all stages of

Protein digestion, specific enzymes, involved in this process, as well as research area specific methodology for protein digestibility evaluation is regularly reviewed. However, as a common rule, the authors of latter group of reviews ignore the existence of methods with similar goals in other research areas, as well as the question of a degree to which any given method represents a living organism. The aim of this review is to describe existing models for evaluation of protein digestibility with a special emphasis on biological relevance

Before a discussion of protein digestibility evaluation methods, it seems worthwhile to introduce a reader to the current understanding of protein and peptide digestion process in order to provide him with a holistic picture of what could happen to an ingested protein. That is why this section will give a short description of digestive tract and an evaluation of enzymes involved in protein digestion in stomach and intestine. A special emphasis will be laid on characterization of intestinal wall peptidases (surface as well as intracellular) with

provided by distinct model as well as on productivity of each methodology.

description of their specificities and their role in overall protein digestion.

**1. Introduction** 

forms that are the most convenient for a patient.

**2. Protein and peptide digestion** 

protein digestion, are used primarily in food quality control.

