Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images

Wireless Capsule Endoscopy is a technique that allows for observation of the entire gastrointestinal tract in an easy and non-invasive way. However, its greatest limitation lies in the time required to analyze the large number of images generated in each examination for diagnosis, which is about 2 hours. This causes not only a high cost, but also a high probability of a wrong diagnosis due to the physician’s fatigue, while the variable appearance of abnormalities requires continuous concentration. In this work, we designed and developed a system capable of automatically detecting blood based on classification of extracted regions, following two different classification approaches. The first method consisted in extraction of hand-crafted features that were used to train machine learning algorithms, specifically Support Vector Machines and Random Forest, to create models for classifying images as healthy tissue or blood. The second method consisted in applying deep learning techniques, concretely convolutional neural networks, capable of extracting the relevant features of the image by themselves. The best results (95.7% sensitivity and 92.3% specificity) were obtained for a Random Forest model trained with features extracted from the histograms of the three HSV color space channels. For both methods we extracted square patches of several sizes using a sliding window, while for the first approach we also implemented the waterpixels technique in order to improve the classification resultsThis work was funded by the European Unions H2020: MSCA: ITN program for the “Wireless In-body Environment Communication WiBEC” project under the grant agreement no. 675353. Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.Pons Suñer, P.; Noorda, R.; Nevárez, A.; Colomer, A.; Pons Beltrán, V.; Naranjo, V. (2019). Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images. En Lecture Notes in Artificial Intelligence. Springer. 105-113. https://doi.org/10.1007/978-3-030-33617-2_12S105113Berens, J., Finlayson, G.D., Qiu, G.: Image indexing using compressed colour histograms. IEE Proc. Vis., Image Signal Process. 147(4), 349–355 (2000). https://doi.org/10.1049/ip-vis:20000630Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324Buscaglia, J.M., et al.: Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study. Clin. Gastroenterol. Hepatol. 6(3), 298–301 (2008). https://doi.org/10.1016/j.cgh.2007.12.029Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018Li, B., Meng, M.Q.H.: Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans. Biomed. Eng. 56(4), 1032–1039 (2009). https://doi.org/10.1109/TBME.2008.2010526Machairas, V., Faessel, M., Cárdenas-Peña, D., Chabardes, T., Walter, T., Decencière, E.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015). https://doi.org/10.1109/TIP.2015.2451011Novozámskỳ, A., Flusser, J., Tachecí, I., Sulík, L., Bureš, J., Krejcar, O.: Automatic blood detection in capsule endoscopy video. J. Biomed. Opt. 21(12), 126007 (2016). https://doi.org/10.1117/1.JBO.21.12.126007Signorelli, C., Villa, F., Rondonotti, E., Abbiati, C., Beccari, G., de Franchis, R.: Sensitivity and specificity of the suspected blood identification system in video capsule enteroscopy. Endoscopy 37(12), 1170–1173 (2005). https://doi.org/10.1055/s-2005-870410Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(1), 91 (2006). https://doi.org/10.1186/1471-2105-7-9

Waterpixels

International audience— Many approaches for image segmentation rely on a 1 first low-level segmentation step, where an image is partitioned 2 into homogeneous regions with enforced regularity and adherence 3 to object boundaries. Methods to generate these superpixels have 4 gained substantial interest in the last few years, but only a few 5 have made it into applications in practice, in particular because 6 the requirements on the processing time are essential but are not 7 met by most of them. Here, we propose waterpixels as a general 8 strategy for generating superpixels which relies on the marker 9 controlled watershed transformation. We introduce a spatially 10 regularized gradient to achieve a tunable tradeoff between the 11 superpixel regularity and the adherence to object boundaries. 12 The complexity of the resulting methods is linear with respect 13 to the number of image pixels. We quantitatively evaluate our 14 approach on the Berkeley segmentation database and compare 15 it against the state-of-the-art

Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Pereira, J.; Colomer, A.; Naranjo Ornedo, V. (2018). Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 174-183. https://doi.org/10.1007/978-3-030-03493-1_19S174183Sidibé, D., Sadek, I., Mériaudeau, F.: Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput. Biol. Med. 62, 175–184 (2015)Zhou, W., Wu, C., Yi, Y., Du, W.: Automatic detection of exudates in digital color fundus images using superpixel multi-feature classification. IEEE Access 5, 17077–17088 (2017)Sinthanayothin, C., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19(2), 105–112 (2002)Walter, T., Klein, J.C., et al.: A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging 21(10), 1236–1243 (2002)Ali, S., et al.: Statistical atlas based exudate segmentation. Comput. Med. Imaging Graph. 37(5–6), 358–368 (2013)Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., et al.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. 18(7), 1026–1043 (2014)Li, H., Chutatape, O.: Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51(2), 246–254 (2004)Welfer, D., Scharcanski, J., Marinho, D.R.: A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput. Med. Imaging Graph. 34(3), 228–235 (2010)Giancardo, L., et al.: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012)Amel, F., Mohammed, M., Abdelhafid, B.: Improvement of the hard exudates detection method used for computer-aided diagnosis of diabetic retinopathy. Int. J. Image Graph. Signal Process. 4(4), 19 (2012)Akram, M.U., Khalid, S., Tariq, A., Khan, S.A., Azam, F.: Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput. Biol. Med. 45, 161–171 (2014)Akram, M.U., Tariq, A., Khan, S.A., Javed, M.Y.: Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Programs Biomed. 114(2), 141–152 (2014)Machairas, V.: Waterpixels and their application to image segmentation learning. Ph.D. thesis, Université de recherche Paris Sciences et Lettres (2016)Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_16Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: TurboPixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)Machairas, V., Faessel, M., Cárdenas-Peña, D., Chabardes, T., Walter, T., Decencière, E.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognit. 43(3), 706–719 (2010)Morales, S., Naranjo, V., Angulo, J., Alcañiz, M.: Automatic detection of optic disc based on PCA and mathematical morphology. IEEE Trans. Med. Imaging 32(4), 786–796 (2013)Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.C., Meyer, F., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)DErrico, J.: inpaint\_nans, matlab central file exchange (2004). http://kr.mathworks.com/matlabcentral/fileexchange/4551-inpaint-nans . Accessed 13 Aug 201

NEW GENERAL FEATURES BASED ON SUPERPIXELS FOR IMAGE SEGMENTATION LEARNING

International audienceSegmenting an image is usually one of the major and most challenging steps in the pipeline of biomedical image analysis. One classical and promising approach is to consider seg-mentation as a classification task, where the aim is to assign to each pixel the label of the objects it belongs to. Pixels are therefore described by a vector of features, where each feature is calculated on the pixel itself or, more frequently, on a sliding window centered on the pixel. In this work, we propose to replace the sliding window by superpixels, i.e. regions which adapt to the image content. We call the resulting features SAF (Superpixel Adaptive Feature). Their contribution is highlighted on a biomedical database of melanocytes images. Qualitative and quantitative analyses show that they are better suited for segmentation purposes than the sliding window approach

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precisionThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by the Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834GB-I00), and Consellería de Educación, Universidade e Formación Profesional (grant number ED431C 2018/19, and accreditation 2019–2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF)S

Diseño y desarrollo de un sistema para la detección automática de sangre en imágenes de cápsula endoscópica

La endoscopia por cápsula inalámbrica permite observar el tracto gastrointestinal completo de forma sencilla y no invasiva. Sin embargo, se genera una gran cantidad de imágenes por examen que los médicos tardan aproximadamente 2 horas en analizar. Esto no solo supone un elevado coste, sino que el diagnóstico puede ser erróneo debido a la fatiga y a la naturaleza variable de las lesiones, que exige una alta concentración. En el presente trabajo se diseña y desarrolla un sistema capaz de detectar automáticamente aquellas imágenes que contienen sangre, siguiendo dos enfoques distintos. El primero consiste en escoger y extraer ciertas características de color de las imágenes con las que entrenar modelos de aprendizaje automático clásico (SVM y Random Forest) que permitan distinguir entre tejido sano y sangre. Además, se implementa la técnica de segmentación “waterpixels” para tratar de mejorarla clasificación. El segundo método consiste en utilizar técnicas de aprendizaje profundo (redes neuronales convolucionales), capaces de extraer las características relevantes de la imagen por sí solas. La configuración que ha obtenido los mejores resultados (95,7% de sensibilidad y 92,3% de especificidad) ha sido un modelo Random Forest entrenado con los histogramas de los canales del espacio de color HSVPons Suñer, P.; Noorda, R.; Naranjo, V.; Nevárez Heredia, A.; Pons Beltrán, V. (2018). Diseño y desarrollo de un sistema para la detección automática de sangre en imágenes de cápsula endoscópica. VISILAB. 257-260. http://hdl.handle.net/10251/136066S25726

Saliency-guided Adaptive Seeding for Supervoxel Segmentation

We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201

Water Body Distributions Across Scales: A Remote Sensing Based Comparison of Three Arctic Tundra Wetlands

Water bodies are ubiquitous features in Arctic wetlands. Ponds, i.e., waters with a surface area smaller than 104 m2, have been recognized as hotspots of biological activity and greenhouse gas emissions but are not well inventoried. This study aimed to identify common characteristics of three Arctic wetlands including water body size and abundance for different spatial resolutions, and the potential of Landsat-5 TM satellite data to show the subpixel fraction of water cover (SWC) via the surface albedo. Water bodies were mapped using optical and radar satellite data with resolutions of 4mor better, Landsat-5 TM at 30mand the MODIS water mask (MOD44W) at 250m resolution. Study sites showed similar properties regarding water body distributions and scaling issues. Abundance-size distributions showed a curved pattern on a log-log scale with a flattened lower tail and an upper tail that appeared Paretian. Ponds represented 95% of the total water body number. Total number of water bodies decreased with coarser spatial resolutions. However, clusters of small water bodies were merged into single larger water bodies leading to local overestimation of water surface area. To assess the uncertainty of coarse-scale products, both surface water fraction and the water body size distribution should therefore be considered. Using Landsat surface albedo to estimate SWC across different terrain types including polygonal terrain and drained thermokarst basins proved to be a robust approach. However, the albedo–SWC relationship is site specific and needs to be tested in other Arctic regions. These findings present a baseline to better represent small water bodies of Arctic wet tundra environments in regional as well as global ecosystem and climate models