A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images

2013 ◽  
Vol 9 (1) ◽  
pp. 21-39 ◽  
Author(s):  
S. Shanthi ◽  
V. Murali Bhaskaran
Keyword(s):  
Feature Extraction ◽  
Cancer Detection ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Benchmark Data ◽  
Optimal Decisions ◽  
Novel Approach ◽  
Step Procedure ◽  
Aided Diagnosis ◽  
Mammogram Images

This study uses data mining techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this paper, an enhanced feature extraction method named Multiscale Surrounding Region Dependence Method (MSRDM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, two dimensional discrete wavelet transformation, and multiscale surrounding region dependence matrix computation and feature extraction. The performance of the proposed feature set is compared with the conventional texture-analysis methods such as gray level cooccurence matrix features and surrounding region dependence method features. Experiments have been conducted on both real and benchmark data and the results have been proved to be progressive.

A Novel Approach of Multiscale Feature Extraction for Gearbox Condition Monitoring

2012 ◽  
Vol 160 ◽  
pp. 25-29
Author(s):  
Wei Guo Huang ◽  
Zhong Kui Zhu ◽  
Cheng Li ◽  
Peng Li
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Work Conditions ◽  
High Accuracy ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Accuracy And Stability

This paper proposes a novel multiscale slope feature extraction method using wavelet-based multiresolution anlaysis for gearboxes fault identification. The new method mainly includes the discrete wavelet transform (DWT), the variances calculation of multiscale detailed signals, and the wavelet-based multiscale slope features estimation. Experimental results show that the wavelet-based multiscale slope features show excellent clustering for different work conditions and have the merits of high accuracy and stability in classifying different conditions of gearbox.

scholarly journals Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations

2020 ◽  
Vol 10 (16) ◽  
pp. 5582
Author(s):  
Xiaochen Yuan ◽  
Tian Huang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Spatial Domain ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Image Postprocessing ◽  
Learning Technique ◽  
Residual Values

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.

scholarly journals A Modified Digital Image Processing Enabled Stroke Rehabilitation System based on Wavelet Transform

2019 ◽  
Vol 9 (1S) ◽  
pp. 234-239
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Stroke Rehabilitation ◽  
Finger Movement ◽  
Discrete Wavelet ◽  
Robotic Hand ◽  
Human Being ◽  
Feature Extraction Method ◽  
Gesture Control

stroke rehabilitation therapy is for people suffering from paralysis. Regular procedures need the involvement of analyst for the duration of the plenary, requires excessive amount. Recently, many approaches have been proposed with control strategy through gesture recognition. The main objective of this work is hand gesture control for stroke rehabilitation based on dwt based feature extraction method. Establishment of artificial machine based computer system assistance provides instruction for paralysis recovery system. The proposed system uses reverse biorthogonal wavelet with two-level decomposition. Preprocessing and image segmentation of real time movement of the hand gestures. Feature extraction is done using discrete wavelet transform (dwt) based approach which has very flexible and adaptable even at the cost of imperfect reconstruction. The prosthetic based robotic hand for human finger movement recovery function. Human being finger movement process helps to rotate motors packed inside the robotic finger section. Therefore, the five finger machine finger movement can imitate the real time human being gesture in a regular manner, which indicates the newly modified machine represents as a education device to provide recovery for the persons affected after paralysis.

scholarly journals Breast Cancer Detection Using Combination of Feature Extraction Models

2018 ◽  
Vol 7 (3.12) ◽  
pp. 848
Author(s):  
T Suneetha Rani ◽  
S J Soujanya ◽  
Pole Anjaiah
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Texture Feature ◽  
Region Of Interest ◽  
Computation Time ◽  
Morphological Operations ◽  
Texture Feature Extraction ◽  
Mammogram Images

Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities.  Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation.  This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

A Cluster-Based Wavelet Feature Extraction Method for Machine Fault Diagnosis

2007 ◽  
Vol 10-12 ◽  
pp. 548-552 ◽  
Author(s):  
Gang Yu ◽  
Chang Ning Li ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Energy Content ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Bearing Fault Diagnosis ◽  
Machine Fault Diagnosis ◽  
The Matrix ◽  
Machine Fault

In this paper, a cluster-based feature extraction from the coefficients of discrete wavelet transform is proposed for machine fault diagnosis. The proposed approach first divides the matrix of wavelet coefficients into clusters that are centered around the discriminative coefficient positions identified by an unsupervised procedure based on the entropy value of coefficients from a set of representative signals. The features that contain the informative attributes of the signals are computed from the energy content of so obtained clusters. Then machine faults are diagnosed based on these feature vectors using a neural network. The experimental results from the application on bearing fault diagnosis have shown that the proposed approach is able to effectively extract important intrinsic information content of the test signals, and increase the overall fault diagnostic accuracy as compared to conventional methods.

scholarly journals Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Vol 12 ◽  
pp. 175628641983868 ◽  
Author(s):  
Yupeng Li ◽  
Jiehui Jiang ◽  
Jiaying Lu ◽  
Juanjuan Jiang ◽  
Huiwei Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Cognitive Impairment ◽  
Fdg Pet ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.

scholarly journals A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

2019 ◽  
Vol 9 (2) ◽  
pp. 4066-4070 ◽  
Author(s):  
A. Mnassri ◽  
M. Bennasr ◽  
C. Adnane
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Real Time ◽  
Extraction Method ◽  
Recognition System ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Speech Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

scholarly journals Coal and Gas Outbursts Prediction Based on DWT+FICA-LDA Feature Extraction and QPSO-DELM Classifier

2020 ◽  
Author(s):  
Xuning Liu ◽  
Zhixiang Li ◽  
Zixian Zhang ◽  
Guoying Zhang
Keyword(s):  
Feature Extraction ◽  
Processing Technique ◽  
Discrete Wavelet ◽  
Data Set ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Fast Independent Component Analysis ◽  
Coal And Gas Outbursts ◽  
Learning Machine ◽  
High Order Statistics

Abstract Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary, the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, Discrete wavelet transform (DWT) is utilized as a processing technique to decompose subseries and extract the features with different frequency, and the optimal feature components are retained; Second, in order to eliminate the redundancy between the features and uncorrelation between feature and outbursts, we use the fast independent component analysis(FICA) feature extraction method based on high-order statistics to obtain each independent feature, obtaining the global information in the feature; then the obtained features are input into linear discriminant analysis(LDA) , under the guidance of class label, then the local information in features are obtained; Finally, the projected features are input into the deep extreme learning machine(DELM) classifier based on the optimal parameters by quantum particle swarm optimization(QPSO) for training and classification. The experimental results on the data set of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators such as speed and recognition effect.

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