Stationary Wavelet Transform(SWT) Based MRI Images Enhancement and Brain Tumor Segmentation

Brain tumor is the anomalous growing of Brain cancer cells. Because of its complex structure, brain tumor segmentation and identification are very difficult tasks in medical field. As with MR image processing, MR images are particularly sensitive to noise, resulting in errors in image acquisition and transmission such as Gaussian noise and impulse noise, etc. MRI image is filtered with Median filter and Wiener filter simultaneously to improve the MR image The Stationary Wavelet Transform (SWT) is then used to combine both Median and Wiener filter results. After preprocessing, Adaptive K-means clustering is used for image segmentation. In the post processing step, morphological operation and Median filter are used to get better segmentation results. This method is applied to the BRATS-2015 dataset, which consists of multi-sequence MRI data available to the public from patients with brain tumors. The well-known, based line methods are compared for comparing the proposed system. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) are used in evaluation of the enhancement. For testing tumor segmentation measures, True Positive Rate (TPR), True Negative Rate (TNR), Accuracy, and Jaccard Similarity Index are used. Compared with dependent line methods and state of the art, this system performs well, especially for the entire tumor area

Determining The Best Entity On Comparison Mining Using Sequential Rule Approaches

With the increased amount of informationrapidly available on the World Wide Web,Internet users that want to know opinions aboutproducts are becoming difficult to determinewhich product (entity) is the best on manyproduct sites. When the product manufacturersare interesting how the product compares withthose of competitors, opinion mining oncomparative sentences becomes very important.Mining on comparative sentences is calledcomparison mining. The purpose of this paper isto get the best entity from superlative relations inthe comparison mining. This paper focuses onmining comparative (opinion) words anddetermines the best entity on comparativesentences from the product reviews data set.Determining the best entity depends on just onefeature that has same nature or applicationdomain. This paper mentions a rule- basedapproach that integrates two sequential rulemining techniques that utilizes POS tagging.Determining the best entity on comparativesentences is effective and time saving, not onlyfor individuals but also for organizations such asbusiness intelligence units

Framework for Audio Fingerprinting based on Discrete Wavelet Entropy

At the core of the presented system is a highlyrobust fingerprint extraction method which enablessearching a large fingerprint database with onlylimited computing resources. Requirements for suchsystems include robustness to a wide range of signaldistortions and availability of fast search methods,even for large fingerprint databases. In this paper anaudio fingerprinting system is presented for songidentification. For the high dimensional audiofingerprint data, audio fingerprint searchingalgorithm were proposed: an audio fingerprintingmethod based on DWE (Discrete wavelet entropy)with timbral features (MFCC and FFT) and anefficient indexing method for Audio fingerprintdatabase using the filtering approach, known also asvector approximation approach which supports thenearest neighbor search efficiently. Spectral subbandentropy is selected due to its resilience againstequalization, compression, and noise addition.Region Approximation Blocks divides a highdimensionalfeature vector space into compact anddisjoined regions. Each region will be approximatedby two bit-strings according to the RA-Blockstechnique

Melanoma Classification on Dermoscopy Skin Images using Bag Tree Ensemble Classifier

Melanoma classification on dermoscopy skin images is a demanding task because of the low contrast of the lesion images, the intra-structural variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation step, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the features according to the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, color and texture features are extracted from the segmented region. Finally, the extracted features are classified to identify whether the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset

Mental Tasks Signal Classification

Electroencephalogram (EEG) signal is animportant source of information for knowingbrain processes. To interpret the brain activity,Matching Pursuit Based EEG signalclassification is proposed. This system includesthree main components which are Preprocessing,Feature extraction and Classification. In thepreprocessing step, Wavelet Packet IndependentComponent Analysis (WPICA) method is used toremove some unwanted noise of EEG recording.Matching Pursuit (MP) with Wavelet PacketDictionary is used to extract the features of EEGsignal. The k Nearest Neighbor (kNN) classifiedthe extracted MP features. In this work, theKeirn and Aunon EEG dataset is used in theexperiments. The feature extracted from MPbased wavelet packet dictionary achieved over90% accuracy in two seconds length ofbrainwave signal in five mental tasksclassification

Segmentation of Skin Lesion towards Melanoma Skin Cancer Classification

Melanoma is one form of skin cancer which is one of the most hazardous types of cancer happened in people. Incidence of skin cancer has been increasing over decades due to excess exposure of radiations from sun causing erosion to skin melanin. The automatic detection of melanoma in dermatological images is a challenging task because of the diverse contrast of skin lesions, the magnitude of melanoma within the class, and the utmost optical similarity to melanoma and lesions other than melanoma and the beingness of many artifacts in the lesion pictures. In this work, the skin lesion analysis system to aid for the melanoma detection is proposed. Firstly, the skin lesion from dermoscopy images is automatically segmented with the use of texture filters. Then, the features according to the underlying ABCD dermatology rules are then extracted from the segmented skin lesion. Finally, the system is classified by random subspace ensemble classifier in order to determine the images as benign or malignant melanoma The performance of the study was experimented with their precision and it achieves with compromising results

Integration of Marker Controlled Watershed and Region Merging Method for Image Segmentation

Automatic image segmentation is a veryimportant task for image analysis, object detectionand recognition tasks. In this research, automaticimage segmentation system is proposed whichincludes three main approaches: preprocessing,segmentation and post processing approach. Thepreprocessing step estimates a better approximationof gradient magnitudes by the modified 7x7Laplacian of Gaussian (LoG) edge filter. Insegmentation step, marker controlled watershedmethod (MCWS) is applied to solve oversegmentation problem. Finally, the segmentedregions are merged by using histogram similarity toobtain the accurate segmented regions in an image.This system is tested on two different kinds ofdatasets: medical image dataset and color naturalimage dataset. In this research, this system has alsoachieved accuracy 93.01% for brain image, 76.72%for color natural image. The running time of theproposed system takes five times than MCWS methodfor medical images due to region merging process formany complex regions

Extracting User’s Interests from Web Log Data for Implementing Adaptive Education System

As World Wide Web is a repository of web pagesand links, it provides not only useful information forthe Internet users but also becomes delivery platformfor searching and surfing day by day. Webpersonalization is the process of customizing a Website to the needs of each specific user or set of users,taking advantage of the knowledge required throughthe analysis of the user’s navigation behavior.Integration usage data with user profile dataenhances the personalization process. In this paper,the adaptive educational system is developed toextract user’s interests from web log data andimplemented the recommender system to suggest thenext links for studying next. The SPADE (SequentialPattern Discovery using Equivalence classes) is usedin finding semantic association rules to overcome theburden of repeated database scans while calculatingthe support of the candidates and DynamicLCS(Longest Common Subsequence) is applied inmapping with users’ current session and associationrules which are generated from the SPADE algorithm.In the proposed system, the teacher and the contentdeveloper are performed their tasks to become themost accurate information for the bestrecommendations by using domain ontology. Themain objective of this proposed system is to analyzethe student’s behavioral patterns to recommend thenew links that best match the individual user’s preferences

Applying Semantic Web Usage Mining in Prediction System

Due to uncontrolled exponential growth in web data, knowledge based retrieval has become a challenging task. The one viable solution to the problem is the merging of conventional web mining with semantic technologies. There are many methods based on web usage mining for prediction system but most of all are based on traditional methods such as sequential pattern mining, clustering and so on. In this research, the reference ontology is built according to the web site structure and the domain ontology is also built according to the clean web log files. Then the domain information is generated and ontology based Perfect Hashing and Shrinking (PHS) algorithm is used in developing sequential pattern. Moreover, the prediction list is produced by applying semantic similarity related with the domain ontology

Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images

Melanoma classification on dermoscopy skin images is a demanding task because of the low contrast of the lesion images, the intra-structural variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation step, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the features according to the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, color and texture features are extracted from the segmented region. Finally, the extracted features are classified to identify whether the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset