Adaptive thresholding in dynamic scene analysis for extraction of fine line

This paper presents an adaptive threshold method whereby a fine thin line of one-pixel width lines could be detected in a gray level images. The proposed method uses the percentage difference between the mean of the pixels within a window and the center pixel. The minimum threshold value however is heuristically set to 32. If the percentage difference is greater than 40% then the threshold value will be set to the difference value. This method has been applied in detecting moving objects with fine lines and the results showed that the method was able to pickup straight thin edges that belong to the moving objec

Asset price bubbles: implications for monetary, regulatory, and international policies

Monetary policy ; Price regulation

Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australias WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australias Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this bands value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scenes resolution. Currently WASARD supports water detection on scenes from ESAs Sentinel-1 and JAXAs ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithms classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are water or not water as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarizations lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled Water and Not Water is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learns LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifiers accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifiers coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. Whil

Gastrointestinal Stromal Tumor (GIST) in Long Standing Crohn’s disease on Anti-TNF Therapy

Introduction Patients suffering from inflammatory bowel disease (IBD) are at increased risk for developing cancer. Adenocarcinomas are the most commonly observed tumors of the gastrointestinal tract whereas data on gastrointestinal stromal tumor (GIST) in IBD patients is limited. GIST is a neoplasm that originates from the interstitial cells of Cajal in the smooth muscle layers of the gastrointestinal tract. [1] The association between GIST and Crohn’s disease (CD) is debated, as the tumor inconsistently present in areas of inflammatory activity. We report an interesting case of CD maintained on Infliximab, who presented with a flare that revealed GIST in the stomach. To our knowledge, this is the first reported occurrence of GIST in stomach in a patient with CD maintained on anti-TNF therapy. Case Report A 40-year-old Caucasian man with a history of small bowel Crohn’s disease on infliximab therapy presented with a two-day history of abdominal pain, hematochezia, and diffuse joint pain. Upon admission, the patient was hemodynamically stable and afebrile, with a blood pressure of 140/70 mmHg, heart rate of 90 beats per minute, and respiratory rate of 14 per minute. Physical exam was remarkable for abdominal distension and diffuse abdominal tenderness. Complete blood count, comprehensive metabolic panel, and C-reactive protein were within normal range. The patient reported no history of alcohol abuse, smoking, recent abdominal procedures, or trauma. The patient had computed tomography (CT) of the abdomen done that revealed a 2.5-centimeter exophytic mass in the stomach with possible liver metastases (Fig. 1). Endoscopic ultrasound (EUS) guided biopsies of the exophytic mass confirmed gastrointestinal stromal tumor (GIST) on fine needle aspiration and flow cytometry results (Fig. 2,3). The patient underwent surgical resection without complication and is back to his usual state of health. Discussion GIST is the most common mesenchymal neoplasm in the gastrointestinal tract [1,2]. The annual incidence of GIST has been reported as 11-19.6 per million [3,4], however a more recent analysis in 2015 estimates the annual incidence to be 6.8 per million with a 53% predominance in males and 73% predominance in Caucasians [5]. Individuals are typically diagnosed with GIST in their seventh decade of life [5]. Immunologically, it is reported that 70-80% of GIST have a mutation in the KIT gene, leading to a continuously active KIT receptor, independent of its activating ligand [1]. KIT activation leads to overexpression of the protein CD117. In KIT-negative GIST, a small number are observed to have a mutation in platelet-derived growth factor receptor-a (PDGFRA). Dysregulated activation of either of these genes results in uncontrolled cell growth and survival. It is estimated that 10-15% of GIST do not have mutations in either KIT or PDGRFA, and while they are considered wild-type, they are shown to express high levels of KIT [1]. More recently, Novelli et al. found that the presence of proteins CD117 and DOG1 had the highest sensitivity and specificity for GIST [6]. The majority of GIST develop in the stomach (60%), with the jejunum and ileum representing the next most common site of involvement (30%) [7]. Several prognostic factors have been researched, most notably tumor location and mitotic index. Emory et al. found that GIST originating from the esophagus had the highest survival rate, followed by those that arose from the stomach, small bowel, colon/rectum, and omentum/mesentery in decreasing order [8]. Additionally, mitotic index, defined as the number of mitotic figures per high-power field (HPF), is reported an independent prognostic factor, with greater than 10 mitotic figures per 50 HPF showing the largest difference in survival in gastric GIST [8]. Small bowel GIST exhibited minimally different survival curves with respect to mitotic index. Age was also found to be an independent prognostic factor of survival in GIST [8]. Later research by Miettinen demonstrated that larger gastric GIST with a diameter of 10cm and 5 mitotic figures per 50 HPF carried a lower metastatic risk in comparison to gastric GIST with diameter of \u3e 5cm but with \u3e 5 mitotic figures per 50 HPF [9]. This may suggest that in gastric GIST, mitotic index carries the most prognostic value. Miettinen found that in intestinal GIST, a diameter of \u3e 5cm and \u3e 5 mitotic figures per HPF each independently carried a moderate or high risk of metastasis, respectively. Intestinal GIST carried a 39% tumor-related mortality rate, compared to 17% for gastric GIST [10,11]. Currently, surgery is the primary treatment modality for nonmetastatic GIST that is technically amenable to resection. Imatinib, a tyrosine kinase inhibitor (TKI), may be used as neoadjuvant therapy or as initial therapy for nonresectable disease [12]. Imatinib directly binds to the KIT protein and prevents further signaling [1]. This medication first demonstrated favorable treatment effects in 2002, with over 50% of the 147 patients showing at least a partial response to therapy [13]. Some patients develop resistance to Imatinib, prompting the development of alternative TKI therapy. Currently, Sunitinib is FDA approved for Imatinib-resistant GIST [14], with a host of other TKI’s and alternative therapies under investigation [1]. In 2012, Körner examined glucagon-like peptide-2 receptor (GLP-2) expression in a variety of neoplasm and found that 68% of the GISTs expressed this receptor in the intestinal myenteric plexus [15]. Additionally, this receptor was expressed in high density in patients with Crohn’s disease. Interestingly, this expression was absent in active or inactive ulcerative colitis as well as Hirschsprung’s disease [15]. Table 1: GIST with concurrent IBD. Author (ref) Age, Sex IBD Symptoms Location of GIST Imaging or operative findings Pfeffela, 1999 [16] 51, M CD Weight loss, Abdominal pain, Fever, Fatigue Ileum Large tumorous lesions in the right lower abdomen (terminal ileum) measuring 8 × 5 × 6 cm Grieco, 2002 [17] 57, F UC Melena, progressive anemia Ileum Solid mass in the left pelvic cavity with a diameter of 7 cm Mijandrusić Sincić, 2005 [18] 81, M CD Ileus Meckel’s diverticulum Dilated loops of intestine with large packets of gas and anti-peristalsis Kaiser, 2006 [19] 64, M UC Severe bleeding, abdominal distension Omentum 8 cm mass attached to greater omentum Ruffolo, 2010 [20] 59, M UC Rectal bleeding Rectum 0.5 cm GIST located 20 cm from anal adenocarcinoma Theodoropoulos, 2009 [21] 45, M CD Abdominal pain, vomiting, constipation, bloating Jejunum and Ileum 6 mm GIST within jejunoileal intussusception Bocker U, 2008 [22] 26, F CD Abdominal cramping, gastrointestinal bleeding Duodenum Ulcerated lesion noted 140 cm past proximal duodenum on enteroscopy Gianluca, 2016 [7] 38, M CD Asymptomatic Small bowel A mass found along the small bowel Gianluca, 2016 [7] 53, M UC Abrupt postoperative bleeding Stomach No evidences of masses at surgery. Gastric bleeding at endoscopy Present paper 40, M CD Abdominal pain, hematochezia Stomach 2.5 cm exophytic mass in the stomach with possible liver metastases CONCLUSION Our case of Crohn’s disease diagnosed with gastric GIST sheds light on a rare link between two separate disease entities native to the gastrointestinal system. While there exists a well-known association between inflammatory bowel disease and colon cancer, other malignancies are described much less frequently in the literature. The development of gastric GIST with underlying Crohn’s disease is a rare occurrence, but is one that should be kept in mind when evaluating patients with inflammatory bowel disease found to have new masses on imaging. References: 1. Corless CL, Barnett CM, Heinrich MC. Gastrointestinal stromal tumours: Origin and molecular oncology. Nat Rev Cancer. 2011;11(12):865-878. doi:10.1038/nrc3143 2. Katzka DA, Loftus E V., Camilleri M. Evolving molecular targets in the treatment of nonmalignant gastrointestinal diseases. Clin Pharmacol Ther. 2012;92(3):306-320. doi:10.1038/clpt.2012.77 3. Nilsson B, Bümming P, Meis-Kindblom JM, et al. Gastrointestinal stromal tumors: The incidence, prevalence, clinical course, and prognostication in the preimatinib mesylate era - A population-based study in western Sweden. Cancer. 2005;103(4):821-829. doi:10.1002/cncr.20862 4. Goettsch WG, Bos SD, Breekveldt-Postma N, Casparie M, Herings RMC, Hogendoorn PCW. Incidence of gastrointestinal stromal tumours is underestimated: Results of a nation-wide study. Eur J Cancer. 2005;41(18):2868-2872. doi:10.1016/j.ejca.2005.09.009 5. Ma GL, Murphy JD, Martinez ME, Sicklick JK. Epidemiology of gastrointestinal stromal tumors in the era of histology codes: Results of a population-based study. Cancer Epidemiol Biomarkers Prev. 2015;24(1):298-302. doi:10.1158/1055-9965.EPI-14-1002 6. Novelli M, Rossi S, Rodriguez-Justo M, et al. DOG1 and CD117 are the antibodies of choice in the diagnosis of gastrointestinal stromal tumours. Histopathology. 2010;57(2):259-270. doi:10.1111/j.1365-2559.2010.03624.x 7. Pellino G, Marcellinaro R, Candilio G, et al. The experience of a referral centre and literature overview of GIST and carcinoid tumours in inflammatory bowel diseases. Int J Surg. 2016;28:S133-S141. doi:10.1016/j.ijsu.2015.12.051 8. Emory TS, Sobin LH, Lukes L, Lee DH, O’Leary TJ. Prognosis of gastrointestinal smooth-muscle (stromal) tumors: Dependence on anatomic site. Am J Surg Pathol. 1999;23(1):82-87. doi:10.1097/00000478-199901000-00009 9. Miettinen M, Lasota J. Gastrointestinal stromal tumors: Pathology and prognosis at different sites. Semin Diagn Pathol. 2006. doi:10.1053/j.semdp.2006.09.001 10. Miettinen M, Sobin LH, Lasota J. Gastrointestinal stromal tumors of the stomach: A clinicopathologic, immunohistochemical, and molecular genetic study of 1765 cases with long-term follow-up. Am J Surg Pathol. 2005;29(1):52-68. doi:10.1097/01.pas.0000146010.92933.de 11. Miettinen M, Makhlouf H, Sobin LH, Lasota J. Gastrointestinal stromal tumors of the jejunum and ileum: A clinicopathologic, immunohistochemical, and molecular genetic study of 906 cases before imatinib with long-term follow-up. Am J Surg Pathol. 2006;30(4):477-489. doi:10.1097/00000478-200604000-00008 12. Demetri GD, Benjamin R, Blanke CD, et al. NCCN Task Force Report: Optimal Management of Patients with Gastrointestinal Stromal Tumor (GIST)--Expansion and Update of NCCN Clinical Practice Guidelines. Vol 2 Suppl 1.; 2004. 13. Eorge D Emetri GD, Argaret Von Ehren MM, Harles B Lanke CD, et al. The New Eng Land Jour Nal of Medicine EFFICACY AND SAFETY OF IMATINIB MESYLATE IN ADVANCED GASTROINTESTINAL STROMAL TUMORS A BSTRACT Background Constitutive Activation of KIT Receptor. Vol 347.; 2002. www.nejm.org. Accessed January 29, 2020. 14. Demetri GD, van Oosterom AT, Garrett CR, et al. Efficacy and safety of sunitinib in patients with advanced gastrointestinal stromal tumour after failure of imatinib: a randomised controlled trial. Lancet. 2006;368(9544):1329-1338. doi:10.1016/S0140-6736(06)69446-4 15. Körner M, Rehmann R, Reubi JC. GLP-2 receptors in human disease: High expression in gastrointestinal stromal tumors and Crohn’s disease. Mol Cell Endocrinol. 2012;364(1-2):46-53. doi:10.1016/j.mce.2012.08.008 16. Pfeffel F, Stiglbauer W, Depisch D, Oberhuber G, Raderer M, Scheithauer W. Coincidence of Crohn’s disease and a high-risk gastrointestinal stromal tumor of the terminal ileum. Digestion. 1999. doi:10.1159/000007684 17. Grieco A, Cavallaro A, Potenza AE, et al. Gastrointestinal stromal tumor (GIST) and ulcerative colitis. J Exp Clin Cancer Res. 2002. 18. Mijandrusic Sincic BM, Kovać D, Jašić M, Grbas H, Uravić M, Depolo A. Crohn’s disease and a gastrointestinal stromal tumor in an 81-year-old man - A rare coincidence. Zentralbl Chir. 2005. doi:10.1055/s-2005-918206 19. Kaiser AM, Kang JC, Tolazzi AR, Sherrod AE, Beart RW. Primary solitary extragastrointestinal stromal tumor of the greater omentum coexisting with ulcerative colitis. Dig Dis Sci. 2006;51(10):1850-1852. doi:10.1007/s10620-006-9217-y 20. Ruffolo C, Massani M, Rossi S, Caratozzolo E, Antoniutti M, Bassi N. Adenocarcinoma and GIST in ulcerative colitis. Int J Colorectal Dis. 2010;25(8):1027-1028. doi:10.1007/s00384-010-0905-x 21. Theodoropoulos GE, Linardoutsos D, Tsamis D, et al. Gastrointestinal stromal tumor causing small bowel intussusception in a patient with Crohn’s disease. World J Gastroenterol. 2009;15(41):5224-5227. doi:10.3748/wjg.15.5224 22. Böcker U, Löhr JM, Marx A. Twenty-six-year-old female with assumed Crohn’s disease and a gastrointestinal stromal tumor: Response. Inflamm Bowel Dis. 2009;15(4):489-490. doi:10.1002/ibd.2065