Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

Revista ew on Brain Tumour Detection using Image Processing

The Automatic Support Intelligent System is used to detect Brain Tumor through the combination of neural network and fuzzy logic system. It helps in the diagnostic and aid in the treatment of the brain tumor. The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents an analytical method that enhances the detection of brain tumor cells in its early stages and to analyze anatomical structures by training and classification of the samples in neural network system and tumor cell segmentation of the sample using fuzzy clustering algorithm. The artificial neural network will be used to train and classify the stage of Brain Tumor that would be benign, malignant or normal. The Fast discrete curvelet transformation is used to analysis texture of an image. In brain structure analysis, the tissues which are WM and GM are extracted. Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. Decision making is performed in two stages: feature extraction using GLCM and the classification using PNN-RBF network. The segmentation is performed by fuzzy logic system and its result would be used as a base for early detection of Brain Tumor which would improves the chances of survival for the patient. The performance of this automated intelligent system evaluates in terms of training performance and classification accuracies to provide the precise and accurate results. The simulated result enhances and shows that classifier and segmentation algorithm provides better accuracy than previous methodologies

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

This study aimed to examine flood hazard zoning and assess the role of check dams as effective hydraulic structures in reducing flood hazards. To this end, factors associated with topographic, hydrologic and human characteristics were used to develop indices for flood mapping and assessment. These indices and their components were weighed for flood hazard zoning using two methods: (i) a multi-criterion decision-making model in fuzzy logic and (ii) entropy weight. After preparing the flood hazard map by using the above indices and methods, the characteristics of the change‐point were used to assess the role of the check dams in reducing flood risk. The method was used in the Ilanlu catchment, located in the northwest of Hamadan province, Iran, where it is prone to frequent flood events. The results showed that the area of ‘very low’, ‘low’ and ‘moderate’ flood hazard zones increased from about 2.2% to 7.3%, 8.6% to 19.6% and 22.7% to 31.2% after the construction of check dams, respectively. Moreover, the area of ‘high’ and ‘very high’ flood hazard zones decreased from 39.8% to 29.6%, and 26.7% to 12.2%, respectively

Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

GEOBIA, TREE DECISION AND HIERARCHICAL CLASSIFICATION FOR MAPPING GULLY EROSION

The gullies provoke environmental, social and financial damages. The application of corrective and preventive measures needs gullies mapping and monitoring. In this scope, this study proposes a methodology for gullies delimitation using object-oriented image analysis. For such, there were used high spatial resolution imagery and ALS data applied for two study areas, one in Uberlandia-Minas Gerais (Brazil) and another one in Queensland (Australia). The objects were generated by multiresolution segmentation. The most important attributes on the delimitation of the gullies were selected using decision tree induction algorithms, being them: spectral, altimetric and texture. Classifications by decision trees and hierarchical were carried out. The use of decision tree allowed the selection of attributes and the establishment of preliminary decision rules. However, since this procedure did not use fuzzy logic, mixtures between classes could not be evidenced in the rule base. Moreover, the classification was performed by a factor of scale only, which did not allow the identification of all the constituent features of the gully. In hierarchical classification, the procedure is performed on different scales, allowing the use of fuzzy logic to describe different degrees of membership in each class, which makes it a very attractive method for cases such as this study, where there is mixing of classes. The classification obtained with hierarchical classification it was more reliable with the field truth, by allowing the use of different scales, uncertainty insert and integration of knowledge, compared to the automatic classification by decision tree

Appearance measurement system using fuzzy logic

To objectively measure the paint appearance on a car body the same as that perceived by customers, this paper presents a novel method to model the paint appearance using fuzzy logic. Three popular parameters, orange peel, metal texture effect and gloss, are used as system input, overall appearance is defined as system output. This fuzzy logic model is fully based on human understanding and expertise The preliminary result shows that this model can well mimic human behavior to reflect the correlation between overall appearance and individual properties of the paint surface

Arguments Whose Strength Depends on Continuous Variation

Both the traditional Aristotelian and modern symbolic approaches to logic have seen logic in terms of discrete symbol processing. Yet there are several kinds of argument whose validity depends on some topological notion of continuous variation, which is not well captured by discrete symbols. Examples include extrapolation and slippery slope arguments, sorites, fuzzy logic, and those involving closeness of possible worlds. It is argued that the natural first attempts to analyze these notions and explain their relation to reasoning fail, so that ignorance of their nature is profound