scholarly journals Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

2020 ◽  
Vol 31 (4) ◽  
pp. 72
Author(s):  
Hayder Adnan AlSudani ◽  
Enaas M. Hussain ◽  
Enam A. Khalil
Keyword(s):  
Multilayer Perceptron ◽  
Region Of Interest ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Features Selection ◽  
Hybrid Features ◽  
Reliable Assessment ◽  
Occurrence Matrix ◽  
Two Stages

Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.

scholarly journals Classification of mammograms based on features extraction techniques using support vector machine

2020 ◽  
Vol 2 (3) ◽  
pp. 121-131
Author(s):  
Enas Mohammed Hussein Saeed ◽  
Hayder Adnan Saleh ◽  
Enam Azez Khalel
Keyword(s):  
Region Growing ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Features Extraction ◽  
Support Vector ◽  
Global Threshold ◽  
Medical City ◽  
Analysis Society ◽  
Occurrence Matrix

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

2013 ◽  
Vol 647 ◽  
pp. 325-330 ◽  
Author(s):  
Yu Fan Zeng ◽  
Xue Jun Zhang ◽  
Wen Yan ◽  
Li Ling Long ◽  
Yu Kun Huang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hepatic Fibrosis ◽  
Region Of Interest ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Support Vector ◽  
Mr Images ◽  
Accuracy Rate ◽  
Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

scholarly journals Classification of Computer Graphic Images and Photographic Images Based on Fusion of Color and Texture Features

2021 ◽  
Vol 35 (3) ◽  
pp. 201-207
Author(s):  
Halaguru Basavarajappa Basanth Kumar ◽  
Haranahalli Rajanna Chennamma
Keyword(s):  
Computer Graphic ◽  
Texture Feature ◽  
Texture Features ◽  
Digital Cameras ◽  
Global Features ◽  
Photographic Images ◽  
Occurrence Matrix ◽  
Paper Classification ◽  
Color And Texture Features

With the rapid advancement in digital image rendering techniques, allows the user to create surrealistic computer graphic (CG) images which are hard to distinguish from photographs captured by digital cameras. In this paper, classification of CG images and photographic (PG) images based on fusion of global features is presented. Color and texture of an image represents global features. Texture feature descriptors such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) are considered. Different combinations of these global features are investigated on various datasets. Experimental results show that, fusion of color and texture features subset can achieve best classification results over other feature combinations.

scholarly journals Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images

2020 ◽  
Author(s):  
Ning Yang ◽  
Faming Liu ◽  
Chunlong Li ◽  
Wenqing Xiao ◽  
Shuangcong Xie ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Svm Model ◽  
Multiple Comparison Correction

Abstract We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Finally, using the radiomics features as an input, a support vector machine (SVM) model was constructed to classify patients with COVID-19 and patients with other pneumonias. This model used 20 rounds of 10-fold cross-validation for training and testing. In the COVID-19 patients, correlation analysis (multiple comparison correction—Bonferroni correction, p<0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The results showed that the proposed method had a classification accuracy as high as 88.33%, sensitivity of 83.56%, specificity of 93.11%, and an area under the curve of 0.947. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some texture features were positively correlated with WBC, NE, and CRP and also negatively related to SPO2H and NE.

Wood Classification Based on Edge Detections and Texture Features Selection

2016 ◽  
Vol 6 (5) ◽  
pp. 2167
Author(s):  
Achmad Fahrurozi ◽  
Sarifuddin Madenda ◽  
Ernastuti Ernastuti ◽  
Djati Kerami
Keyword(s):  
Edge Detection ◽  
Wood Quality ◽  
Texture Features ◽  
Visual Observation ◽  
Features Selection ◽  
Wood Fibers ◽  
Fiber Density ◽  
Cross Sectional ◽  
Quality Classification ◽  
Occurrence Matrix

<p>One of the properties of wood is a mechanical property, includes: hardness, strength, cleavage resistance, etc. Among these properties there that can be measured or estimated by visual observation on cross-sectional areas of wood, which is based on inter-fiber density, fiber size, and lines that build the annual rings. In this paper, we proposed a new wood quality classification method based on edge detections. Edge detection is applied to the wood test images with the aim to improving the characteristics of wood fibers so as to make it easier to distinguish their quality. Gray Level Co-occurrence Matrix (GLCM) used to obtain wood texture features, while the wood quality classification done by Naïve Bayes classifier.<em> </em>Found in our experimental results that the first-order edge detection is likely to provide a good accuracy rate and precision. The second order edge detection is highly dependent on the choice of parameters and tends to give worse classification results, as filtering the original wood image, thus blurring characteristics related to wood density. Selection of features obtained from co-occurrence matrix is also quite affected the classification results.</p>

Spectrum Classification of Easily Confused Ground Objects in ALI Remote Sensing Image Based on Texture Features

2012 ◽  
Vol 610-613 ◽  
pp. 3606-3611
Author(s):  
Ling Ling Zhang ◽  
Ge Ying Lai ◽  
Xiang Gui Zeng ◽  
Fa Zhao Yi
Keyword(s):  
Maximum Likelihood Method ◽  
Research Result ◽  
Principal Component ◽  
Window Size ◽  
Texture Features ◽  
Likelihood Method ◽  
Forest Land ◽  
Classification Result ◽  
Occurrence Matrix

According to the problem that the classification result of shrub and forest land was easy to confuse when used spectrum of Advanced Land Image (ALI) to classify. This paper used the Meijiang River watershed as the study area. Used the Principal Component Analysis (PCA) to reduce dimension, taken the Contrast, Second moment, Mean and Dissimilarity as the texture values, and extracted the texture by Gray level co-occurrence matrix (GLCM). The texture features extracted from different window sizes were used the Maximum likelihood method to classify, and chosen the texture features extracted by the most suitable window size to join the classification. The research result shows that the texture features extracted by window size of 11×11 can distinguish well the two easily ground objects; moreover, the overall accuracy of classification used texture and spectrum features reached to 87.55%, which is 4.4% higher than the classification with spectrum.

scholarly journals NIMG-76. RADIOPATHOMICS: INTEGRATION OF RADIOGRAPHIC AND HISTOLOGIC CHARACTERISTICS FOR PROGNOSTICATION IN GLIOBLASTOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi178-vi179 ◽  
Author(s):  
Saima Rathore ◽  
MacLean Nasrallah ◽  
Zissimos Mourelatos
Keyword(s):  
Data Streams ◽  
De Novo ◽  
Pearson Correlation ◽  
Digital Pathology ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
Biomedical Data ◽  
Clinical Workflow ◽  
Occurrence Matrix

Abstract INTRODUCTION Large number of diverse imaging [e.g., multi-parametric MRI (mpMRI), and digital pathology images] and non-imaging (e.g., clinical) biomedical data streams are being routinely acquired as part of the standard clinical workflow for glioblastoma patients. However, under the current clinical practice, these data streams are not collectively used for diagnosis. We sought to assess the synergies between pathologic, and radiomic features by evaluating the predictive value of each group of features and their combinations through a prognostic classifier. METHODS The mpMRI (T1,T1-Gd,T2,T2-FLAIR) and corresponding digital pathology images for 135 de novo glioblastoma was acquired from TCIA. An extensive panel of handcrafted features, including shape, volume, intensity distributions, gray-level co-occurrence matrix based texture, was extracted from delineated tumor regions of mpMRI scans. A set of 100 region-of-interest each comprising 1024x1024 that contained viable tumor with descriptive histologic characteristics and that were free of artifacts were extracted from digital pathology images, and were quantified in terms of nuclear texture features, and nuclear intensity and gradient statistics. A support vector regression multivariately integrated these features towards a marker of overall-survival. The accuracy of the predictive model for each group of features, and their combinations, was determined via a 10-fold cross-validation scheme. RESULTS The Pearson correlation coefficient between the survival scores predicted by SVR and the actual survival scores was estimated to be 0.75 and 0.77 for radiographic and pathologic data, however, the integration of these data yielded a clear improvement in correlation (0.81), supporting the synergistic value of these features in the prognostic model. CONCLUSION Radiomic features extracted from preoperative mpMRI, when used together with digital pathology features, offer synergistic value in assessment of prognosis in individual patients. The proposed radiopathomics marker may contribute to (i) stratification of patients into clinical trials, (ii) patient selection for targeted therapy, and (iii) personalized treatment planning.

scholarly journals Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

Cancers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1937 ◽  
Author(s):  
Subrata Bhattacharjee ◽  
Cho-Hee Kim ◽  
Hyeon-Gyun Park ◽  
Deekshitha Prakash ◽  
Nuwan Madusanka ◽  
...  
Keyword(s):  
Texture Features ◽  
Haar Wavelet ◽  
Histological Sections ◽  
Colour Features ◽  
R Programming ◽  
Grade 3 ◽  
Occurrence Matrix ◽  
Mlp Classifier ◽  
Prostate Carcinomas

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.

MULTI-FEATURE-BASED SEGMENTATION OF SONOELASTOGRAPHIC BREAST IMAGES

2013 ◽  
Vol 09 (01) ◽  
pp. 1250005
Author(s):  
A. SINDHUJA ◽  
V. SADASIVAM
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Mortality ◽  
Region Of Interest ◽  
Texture Features ◽  
Extraction Methods ◽  
Morphological Processing ◽  
Optimum Number ◽  
Classification Problems ◽  
Segmented Images ◽  
Occurrence Matrix

Breast cancer is the leading cause of death in women. Early detection and early treatment can significantly reduce the breast cancer mortality. Texture features are widely used in classification problems, i.e., mainly for diagnostic purposes where the region of interest is delineated manually. It has not yet been considered for sonoelastographic segmentation. This paper proposes a method of segmenting the sonoelastographic breast images with optimum number of features from 32 features extracted from three different extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Edge-Based Features. The image undergoes preprocessing by Sticks filter that improves the contrast and enhances the edges and emphasizes the tumor boundary. The features are extracted and then ranked according to the Sequential Forward Floating Selection (SFFS). The optimum number of ranked features is used for segmentation using k-means clustering. The segmented images are subjected to morphological processing that marks the tumor boundary. The overall accuracy is studied to investigate the effect of automated segmentation where the subset of first 10 ranked features provides an accuracy of 79%. The combined metric of overlap, over- and under-segmentation is 90%. The proposed work can also be considered for diagnostic purposes, along with the sonographic breast images.

scholarly journals Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

2021 ◽  
pp. 435-439
Author(s):  
Rajani Kumari ◽  
C. Thanuja ◽  
K. Sai Thanvi ◽  
K. Lakshmi ◽  
U. Lavanya
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
Lung Nodules ◽  
Shape Features ◽  
Rule Based ◽  
Occurrence Matrix

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

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