Laws Texture Research Articles

Argument by Numbers: The Normative Impact of Statistical Legal Tech

The introduction of statistical ‘legal tech’ raises questions about the future of law and legal practice. While technologies have always mediated the concept, practice, and texture of law, a qualitative and quantitative shift is taking place: statistical legal tech is being integrated into mainstream legal practice, and particularly that of litigators. These applications – particularly in search and document generation – mediate how practicing lawyers interact with the legal system. By shaping how law is ‘done’, the applications ultimately come to shape what law is. Where such applications impact on the creative elements of the litigator’s practice, for example via automation bias, they affect their professional and ethical duty to respond appropriately to the unique circumstances of their client’s case – a duty that is central to the Rule of Law. The statistical mediation of legal resources by machine learning applications must therefore be introduced with great care, if we are to avoid the subtle, inadvertent, but ultimately fundamental undermining of the Rule of Law. In this contribution we describe the normative effects of legal tech application design, how they are potentially (in)compatible with law and the Rule of Law as normative orders, particularly with respect to legal texts which we frame as the proper source of ‘lossless law’, uncompressed by statistical framing. We conclude that reliance on the vigilance of individual lawyers is insufficient to guard against the potentially harmful effects of such systems, given their inscrutability, and suggest that the onus is on the providers of legal technologies to demonstrate the legitimacy of their systems according to the normative standards inherent in the legal system.

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach

ObjectivesTo evaluate the performance of machine learning–augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas.MethodsForty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning–augmented radiomics analyses.ResultsThough only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22–0.58) and 0.44 (95% CI 0.26–0.62) for RF and AdaBoost, respectively.ConclusionOverall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

Цифровые технологии и правовая аргументация: влияние статистических юридических технологий на право

The introduction of statistical “legal tech” raises questions about the future of law and legal practice. While technologies have always mediated the concept, practice, and texture of law, a qualitative and quantitative shift is taking place: statistical legal tech is being integrated into mainstream legal practice, and particularly that of litigators. These applications — particularly in search and document generation — mediate how practicing lawyers interact with the legal system. By shaping how law is “done”, the applications ultimately come to shape what law is. Where such applications impact on the creative elements of the litigator’s practice, for example via automation bias, they affect their professional and ethical duty to respond appropriately to the unique circumstances of their client’s case — a duty that is central to the Rule of Law. The statistical mediation of legal resources by machine learning applications must therefore be introduced with great care, if we are to avoid the subtle, inadvertent, but ultimately fundamental undermining of the Rule of Law. In this contribution we describe the normative effects of legal tech application design, how they are (in)compatible with law and the Rule of Law as normative orders, particularly with respect to legal texts which we frame as the proper source of “lossless law”, uncompressed by statistical framing. We conclude that reliance on the vigilance of individual lawyers is insufficient to guard against the potentially harmful effects of such systems, given their inscrutability, and suggest that the onus is on the providers of legal technologies to demonstrate the legitimacy of their systems according to the standards inherent in the legal system. The translation and publication of this article is based on the CC BY Attribution-NonCommercial 4.0 International license, under which this article was published in English at https://osf.io/preprints/socarxiv/ts259/. The article is accepted for publication in Communitas (2022).

MRI-Derived Radiomics of Hemophilic Arthropathy Is Associated with Severity of Joint Disease

IntroductionJoint bleeding resulting in synovial hypertrophy and articular cartilage damage are the hallmarks of hemophilic arthropathy. Early prophylaxis with extended half-life factor products and non-factor replacement therapies reduce joint bleeds but do not provide complete protection from development of arthropathy. MRI is the gold standard imaging technique to diagnose and monitor arthropathy in hemophilia. MRI changes that are associated with joint bleeding and development of arthropathy, however, are not sensitive enough to detect early changes in the synovial membranes after sub-clinical bleeding. Radiomics, the computerized extraction of sub-visual attributes from imaging scans can detect early changes of hemophilic arthropathy and help in more accurate characterizations of severe joint disease. The goal of this project was to develop and compare MRI radiomics with the International Prophylaxis Study Group (IPSG) score, an established MRI joint disease scale, in assessing the severity of joint disease.MethodsAfter IRB approval, knee MRI scans of 17 hemophilia patients with a history of joint bleeding and 12 age-matched healthy controls were included in this study. Manual annotations were performed in consensus by a board-certified musculoskeletal radiologist to include the prefemoral fat, suprapatellar fat pad and bursa . To ensure that the signal intensities from different cases were in tissue-specific correspondence, a landmark-based histogram transformation was used to align MRI signal intensity distributions across all cases. 560 radiomic features capturing MRI pixels texture heterogeneity (Gabor wavelets, Laws texture, Haralick and GoLIAGe co-occurrence patterns) were subsequently extracted on all MRI slices with ROI for all the cases. Top 2 radiomic features (F 2) for differentiating between abnormal joint changes and control were selected by Minimum Redundancy Maximum Relevance (mRMR) algorithm with 100 iterations of 3-fold cross-validation. Consensus hierarchical clustering was then applied on F 2, from which two patient clusters were generated. A disease severity score was calculated based on historical lifetime bleed events in the joint, designation of target joint status, and history of surgical synovectomy. A previously described 17-point MRI IPSG score was also calculated by 2 independent reviewers. Both the IPSG score and the F 2 were then fit into a linear regression model separately as predictors whereas the disease severity score was set to be the response variable. The Akaike's Information Criteria (AIC) and the root mean square error (RMSE) were used to evaluate the model fitness and the likelihood ratio (LR) test was applied for model comparison.ResultsThe dataset consisted of 17 males with moderate and severe hemophilia ranging in age from 10-55 years. The median disease severity score was 2 (0-11) and the median IPSG MRI Score was 2 (0-11). The top 2 radiomic features F 2, a Gabor wavelet feature and a Lawshc texture feature, yielded an AUC of 0.94 and an accuracy of 0.9 for differentiating between hemophilic joint changes and healthy control patients. Unsupervised clustering demonstrated good separation between the two classes (Fig panel A and B). The radiomic-based linear regression model (AIC: 86.67; RMSE: 2.86) was significantly better than the IPSG score-based model (AIC: 94.24; RMSE: 3.66) in joint disease severity characterizations (LR, p = 0.0083, Fig panel C).ConclusionsRadiomic analysis of knee MRI in hemophilia patients with history of joint bleeding is correlated with the severity of joint disease. Our study demonstrates the feasibility of developing and utilizing a radiomics based tool to detect the severity of joint damage in a small population of knee joint hemophilic arthropathy. We plan to perform an Independent validation of the radiomics signature in a larger dataset of hemophilia joint MRI scans [Display omitted] DisclosuresMadabhushi: Astrazeneca: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers-Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Caris: Consultancy; Aiforia: Consultancy; Philips: Research Funding; AstraZeneca: Research Funding; Boehringer-Ingelheim: Research Funding; Bristol Meyers-Squibb: Research Funding. Ahuja: Sanofi: Membership on an entity's Board of Directors or advisory committees; XaTek, Inc: Patents & Royalties; Genentech: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: DSMB member .

The Many Metamorphoses of Related Rights in EU Copyright Law: Unintended Consequences or Inevitable Developments?

Abstract Neighboring rights have always been neglected by copyright scholars, despite a steady increase of their economic relevance and market role across the decades. The situation has changed in the past two decades when, in response to the epochal shifts to which such rights have been subject, commentators have transferred their focus to the legislative and judicial evolution of neighboring rights. These changes, in fact, have had a substantial impact on the texture of EU copyright law and of the common core of EU Member States’ laws, which may – or may not – represent the beginning of a new era for the relationship between copyright and related rights. To understand the drivers and purpose of the various legislative reforms and the CJEU’s responses in the field, and the challenges they have posed to the consistency and internal balance of the EU copyright system, it is essential to analyze their roots and key features. To this end, this article will offer a brief overview of the legislative path that led from the Rome Convention to the debate preceding the Directive on Copyright in the Digital Single Market (CDSMD), with a focus on the introduction of sui generis rights and ancillary copyright (Section II). It will then comment on the nature and features of the new press publishers’ right under Art. 15 CDSMD, focusing on its strengths and pitfalls (Section III). The statutory analysis will be coupled with a comment on the four ‘waves’ of the CJEU’s judicial practice (Section IV), leading to a brief assessment and conclusions on the way forward (Section V).

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

A Contrast Enhancement Method for Improving Surface Defects Detection of Elevator Compensation Chain

A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.

Artificial neural networks and electron microscopy to evaluate the quality of fruit and vegetable spray-dried powders. Case study: Strawberry powder

The increasing trend of consumer awareness regarding balanced nutrition, methods of food preparation and processing as well as an increased attention to quality of products by customers have resulted in the phenomena of searching for new methods of improving the quality of product and automatization of technological processes.The research was based on image recognition of qualitative classes of powders obtained by the use of the process called industrial spray-drying. Structures of spray dried strawberry juice were analyzed using scanning electron microscopy and a digital camera. To this end, Gray-Level Co-occurrence (GLCM) and Laws texture analysis were used. The final outcome obtained as a result of the above tests demonstrated that the analysis of quality properties of texture made it possible to effectively distinguish research material consisting of various spray-dried powders of strawberry juice. The analysis of Artificial Neural Networks showed that there are adequate models in terms of the lowest error value RMS (Root Mean Square) and effectiveness classifying wrong structure of particles in assessment of the qualitative strawberry powders.The analysis of key components demonstrated that texture parameters correlate well with color feature in the technique called computer image analysis. More strongly correlated variables were presented in GLCM texture parameters in scanning electron microscopy. A vital correlation depended on recognition of microstructure of particles with regard to shape such as Ferret factor, roundness factor or circularity factor in electron microscopy.These results confirm that recognition of qualitative classes of powders can constitute the basis for quick evaluation of the quality of fruit powders in industrial spray drying.

Enhanced Laws textures: A potential MRI surrogate marker of hepatic fibrosis in a murine model

PurposeTo compare enhanced Laws textures derived from parametric proton density (PD) maps to other MRI surrogate markers (T2, PD, apparent diffusion coefficient (ADC)) in assessing degrees of liver fibrosis in an ex vivo murine model of hepatic fibrosis imaged using 11.7T MRI. MethodsThis animal study was IACUC approved. Fourteen male, C57BL/6 mice were divided into control and experimental groups. The latter were fed a 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC) supplemented diet to induce hepatic fibrosis. Ex vivo liver specimens were imaged using an 11.7T scanner, from which the parametric PD, T2, and ADC maps were generated from spin-echo pulsed field gradient and multi-echo spin-echo acquisitions. A sequential enhanced Laws texture analysis was applied to the PD maps: automated dual-clustering algorithm, optimal thresholding algorithm, global grayscale correction, and Laws texture features extraction. Degrees of fibrosis were independently assessed by digital image analysis (a.k.a. %Area Fibrosis). Scatterplot graphs comparing enhanced Laws texture features, T2, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated. ResultsHepatic fibrosis and the enhanced Laws texture features were strongly correlated with higher %Area Fibrosis associated with higher Laws textures (r=0.89). Without the proposed enhancements, only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture features (r=0.70). Correlation also existed between %Area Fibrosis and ADC (r=0.86), PD (r=0.65), and T2 (r=0.66). ConclusionsHigher degrees of hepatic fibrosis are associated with increased Laws textures. The proposed enhancements could improve the accuracy of Laws texture features significantly.

Prototype system for feature extraction, classification and study of medical images

Colonoscopy exam images are useful to identify diseases, such as the colorectal cancer, which is one of the most common cancers worldwide. Computational image analysis and machine learning techniques can assist experts to identify abnormalities in these images. In this work, we present and evaluate MIAS 3.0, which aims to help experts to study and analyze colon tissue images. To do so, the system initially extracts features from these images. Currently, Amadasum, Haralick and Laws texture descriptors are supported. Then, the described images are classified into normal or abnormal images. In this version, J48, nearest neighbor, backpropagation based on multilayer perceptron, naive Bayes, and support vector machine classification algorithms are implemented. MIAS was developed with open source technologies using a software engineering approach to improve flexibility and maintainability. In this work, MIAS was quantitatively assessed by its application in a set of 134 tissue image fragments. The classifiers built from this set were compared according to the cross-validation and contingency table strategies. Also, the system was qualitatively evaluated using 12 heuristics by twelve volunteers from Health and Exact Sciences. The issues found were categorized according to Rolf Molich’s severity scale. As a result, the J48 classifier achieved the highest sensitivity (85.07%) and reasonable average error (18.68%). In the qualitative evaluation, 61.26% of the issues found were not considered serious. These assessments suggest that MIAS can be useful to assist domain experts with minimum knowledge in informatics to conduct more complete studies of medical images, by identifying patterns regarding different abnormalities.

WE‐FG‐206‐12: Enhanced Laws Textures: A Potential MRI Surrogate Marker of Hepatic Fibrosis in a Murine Model

Purpose:To compare enhanced Laws texture derived from parametric proton density (PD) maps to other MRI‐based surrogate markers (T2, PD, ADC) in assessing degrees of liver fibrosis in a murine model of hepatic fibrosis using 11.7T scanner.Methods:This animal study was IACUC approved. Fourteen mice were divided into control (n=1) and experimental (n=13). The latter were fed a DDC‐supplemented diet to induce hepatic fibrosis. Liver specimens were imaged using an 11.7T scanner; the parametric PD, T2, and ADC maps were generated from spin‐echo pulsed field gradient and multi‐echo spin‐echo acquisitions. Enhanced Laws texture analysis was applied to the PD maps: first, hepatic blood vessels and liver margins were segmented/removed using an automated dual‐clustering algorithm; secondly, an optimal thresholding algorithm was applied to reduce the partial volume artifact; next, mean and stdev were corrected to minimize grayscale variation across images; finally, Laws texture was extracted. Degrees of fibrosis was assessed by an experienced pathologist and digital image analysis (%Area Fibrosis). Scatterplots comparing enhanced Laws texture, T2, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated. Unenhanced Laws texture was also compared to assess the effectiveness of the proposed enhancements.Results:Hepatic fibrosis and the enhanced Laws texture were strongly correlated with higher %Area Fibrosis associated with higher Laws texture (r=0.89). Only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture (r=0.70). Strong correlation also existed between ADC and %Area Fibrosis (r=0.86). Moderate correlations were seen between %Area Fibrosis and PD (r=0.65) and T2 (r=0.66).Conclusions:Higher degrees of hepatic fibrosis are associated with increased Laws texture. The proposed enhancements improve the accuracy of Laws texture. Enhanced Laws texture features are more accurate than PD and T2 in assessing fibrosis, and can potentially serve as an accurate surrogate marker for hepatic fibrosis.

World War I and the Appearance of Aerial Warfare: A Lacuna in the Texture of International Law?

World War I and the Appearance of Aerial Warfare: A Lacuna in the Texture of International Law?

A new indexing technique to retrieve images using integration of colour-size, texture and shape features

The purpose of this paper is to describe the designing of a content-based image retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this paper outlines a description of the primitive features like colour, texture and shape. Colour-size, novel feature is the combination of grey level histogram values and number of segments of the image. Texture features are extracted using LAWS texture features and shape features are retrieved using Zernike moments. After the extraction of primitive image features, the proposed kd-ORS tree is used for indexing the images in the database. The most relevant images are retrieved using the proposed kd-ORS tree. It is experimentally found that the proposed kd-ORS tree outperforms the other existing indexing methods.

Rice Paper Classification Study Based on Signal Processing and Statistical Methods in Image Texture Analysis

Texture analysis plays an important role in image processing. In the field of texture analysis, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. This paper brings texture analysis into the study of rice paper's classification. First of all it shows the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods it chooses the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When it uses the two texture analysis methods separately, the classification accuracy of rice paper is lower, so it tries to combine the two texture analysis methods. The experimental results show that the classification result got with two combined texture analysis methods is better than that got with one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.

Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.

Abstract P2-03-01: Computer extracted image texture features on T2-weighted MRI appear to correlate with nuclear morphologic descriptors from H&E-stained histopathology in estrogen receptor positive breast cancers

Abstract Oncotype DX (ODX) is a 21 panel gene-expression based assay for predicting whether patients with estrogen receptor-positive (ER+) breast cancer (BCa) are candidates for adjuvant chemotherapy. However, the time and expense associated with genomic assays suggests the need for a non-invasive, imaging-based, pre-therapeutic tool for assessment of disease risk and selection of an appropriate treatment regimen. The objective of this research was to determine whether (a) computer extracted image features on T2-weighted (T2w) MRI and H&E stained histopathology are independently able to distinguish ER+ BCa with low and high ODX recurrence scores (RS) and (b) to determine whether there is a correlation between MRI and histologic features identified as being predictive of low and high ODX risk categories. A total of 11 ER+ BCa patients were considered in this study, based on availability of in vivo 1.5 Tesla T2w MRI. For each study, the corresponding formalin-fixed paraffin-embedded H&E stained tissue specimens were digitized at 20x (0.5 μm/pixel) using a whole-slide scanner. Of the 11 patients, 8 were identified in the low ODX (RS < 18) and 3 in the high ODX (RS > 30) risk categories. Each dataset was accompanied by expert annotations of (a) the lesion ROI on MRI and (b) boundaries of epithelial nuclei from a representative field-of-view on the digitized histology slide. For each MRI study, a multi-scale, multi-orientation Gabor filter bank was convolved with the annotated lesion area providing a set of 192 texture features (FMRI). For each corresponding histology image, 471 features (FHIST) were extracted describing both nuclear morphology (NM) and Laws texture (LT) within the nuclear regions. Independent 2-sample t-tests were used to identify salient features in FMRI and FHIST that are able to distinguish low and high ODX risk categories. We found that, for the MRI dataset, Gabor texture features at several scales and orientations yielded salient features (p < 0.05) while on histopathology, nuclear texture and convexity (shape) features were identified as the top discriminative features (p < 0.01). Relationships between significant features were evaluated via Spearman's rank correlation test (see table), where high correlations were observed between lesion texture on T2w MRI and nuclear texture and shape on histology. Correlation of histologic and MRI features able to distinguish low and high ODX RSHistologic feature correlated with ODXMRI feature correlated with ODXCorrelation coefficient (ρ)p-valueLT: 70 Mean HSVGF: Scale 2: Orientation 3: min/max-0.85450.0008NM: ConvexityGF: Scale 5: Orientation 6: mean-0.85450.0008LT: 70 Mean HSVGF: Scale 2: Orientation 3: min/max-0.83640.0013LT: 70 Mean HSVGF: Scale 3: Orientation 8: mean-0.83640.0013LT: 70 Mean HSVGF: Scale 3: Orientation 2: mean-0.81820.0021 Our results suggest that quantitative features extracted on both T2w MRI and histopathology can independently distinguish between low and high risk ODX classes. Moreover, some of these MRI and histologic features appear to be significantly correlated, suggesting that information regarding tumor biology is reflected in both MRI and histologic image features. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-03-01.

ON THE OPEN TEXTURE OF LAW

This paper, prepared for the University of Frankfurt Symposium on Defeasibility in Epistemology, Ethics, law and Logic, addresses the claim of H.L.A. Hart and others that law is open-textured. It is in the nature of law, they say, that it necessarily possesses an open texture going beyond the open texture of the language in which legal rules are written. But when we examine the question of open texture in light of Hart’s claim that the open texture of law entails the necessary defeasibility of legal rules, we discover that Hart and his followers are mistaken. Both the alleged open texture of law qua law (as opposed to the open texture occasioned by the open texture of the language used by law) and the defeasibility of legal rules are contingent features of certain legal rules in certain legal regimes, but neither are necessary components of the nature of law or the nature of rules.

Savignys Lehre von der Auslegung der Gesetze in der Zeit der Dekonstruktion

This essay poses the question whether it is possible, given the relationship of a subject and the method of its examination, at present as opposed to the time when Savigny worked, to talk about the same subject, and if it is not, what are the consequences for the interpretation of law. The turbulent history of Europe in the 20th century, marked by the cataclysms of world wars, genocides, expulsions and upheavals, therefore the necessity of new beginnings, jointly with other factors (e.g. technological optimism) resulted not only in the ebbing of influence for religion, morality and tradition, but also in a deconstruction of law. This includes fragmentation, loss of the ability to formulate and interpret the purpose of law, as well as relativization of the traditional ordering principles of law (particularly the non-contradiction condition and contextual consistency, hierarchical structure, relative stability of law and its language). The opening of the texture of law, its deconstruction, creates a new segment of legislation, namely judicial law-making arising from the uncertainty of normative premises.

Ovarian Tumor Characterization using 3D Ultrasound

Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Preoperative determination of whether a tumor is benign or malignant has often been found to be difficult. Because of such inconclusive findings from ultrasound images and other tests, many patients with benign conditions have been offered unnecessary surgeries thereby increasing patient anxiety and healthcare cost. The key objective of our work is to develop an adjunct Computer Aided Diagnostic (CAD) technique that uses ultrasound images of the ovary and image mining algorithms to accurately classify benign and malignant ovarian tumor images. In this algorithm, we extract texture features based on Local Binary Patterns (LBP) and Laws Texture Energy (LTE) and use them to build and train a Support Vector Machine (SVM) classifier. Our technique was validated using 1000 benign and 1000 malignant images, and we obtained a high accuracy of 99.9% using a SVM classifier with a Radial Basis Function (RBF) kernel. The high accuracy can be attributed to the determination of the novel combination of the 16 texture based features that quantify the subtle changes in the images belonging to both classes. The proposed algorithm has the following characteristics: cost-effectiveness, complete automation, easy deployment, and good end-user comprehensibility. We have also developed a novel integrated index, Ovarian Cancer Index (OCI), which is a combination of the texture features, to present the physicians with a more transparent adjunct technique for ovarian tumor classification.

Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems

Ultrasound-based thyroid nodule characterization into benign and malignant types is limited by subjective interpretations. This paper presents a Computer Aided Diagnostic (CAD) technique that would present more objective and accurate classification and further would offer the physician a valuable second opinion. In this paradigm, we first extracted the features that quantify the local changes in the texture characteristics of the ultrasound off-line training images from both benign and malignant nodules. These features include: Fractal Dimension (FD), Local Binary Pattern (LBP), Fourier Spectrum Descriptor (FS), and Laws Texture Energy (LTE). The resulting feature vectors were used to build seven different classifiers: Support Vector Machine (SVM), Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (KNN), Radial Basis Probabilistic Neural Network (RBPNN), and Naive Bayes Classifier (NBC). Subsequently, the feature vector-classifier combination that results in the maximum classification accuracy was used to predict the class of a new on-line test thyroid ultrasound image. Two data sets with 3D Contrast-Enhanced Ultrasound (CEUS) and 3D High Resolution Ultrasound (HRUS) images of 20 nodules (10 benign and 10 malignant) were used. Fine needle aspiration biopsy and histology results were used to confirm malignancy. Our results show that a combination of texture features coupled with SVM or Fuzzy classifiers resulted in 100% accuracy for the HRUS dataset, while GMM classifier resulted in 98.1% accuracy for the CEUS dataset. Finally, for each dataset, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI) using the combination of FD, LBP, LTE texture features, to diagnose benign or malignant nodules. This index can help clinicians to make a more objective differentiation of benign/malignant thyroid lesions. We have compared and benchmarked the system with existing methods.