Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis

2020 ◽  
Vol 65 (1) ◽  
pp. 87-98
Author(s):  
Qi Zhang ◽  
Jingyu Xiong ◽  
Yehua Cai ◽  
Jun Shi ◽  
Shugong Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Diagnosis ◽  
Feature Learning ◽  
Texture Features ◽  
Disease Classification ◽  
Support Vector ◽  
Restricted Boltzmann Machines ◽  
Deep Network ◽  
Multimodal Features ◽  
Occurrence Matrix

AbstractB-mode ultrasonography and sonoelastography are used in the clinical diagnosis of prostate cancer (PCa). A combination of the two ultrasound (US) modalities using computer aid may be helpful for improving the diagnostic performance. A technique for computer-aided diagnosis (CAD) of PCa is presented based on multimodal US. Firstly, quantitative features are extracted from both B-mode US images and sonoelastograms, including intensity statistics, regional percentile features, gray-level co-occurrence matrix (GLCM) texture features and binary texture features. Secondly, a deep network named PGBM-RBM2 is proposed to learn and fuse multimodal features, which is composed of the point-wise gated Boltzmann machine (PGBM) and two layers of the restricted Boltzmann machines (RBMs). Finally, the support vector machine (SVM) is used for prostatic disease classification. Experimental evaluation was conducted on 313 multimodal US images of the prostate from 103 patients with prostatic diseases (47 malignant and 56 benign). Under five-fold cross-validation, the classification sensitivity, specificity, accuracy, Youden’s index and area under the receiver operating characteristic (ROC) curve with the PGBM-RBM2 were 87.0%, 88.8%, 87.9%, 75.8% and 0.851, respectively. The results demonstrate that multimodal feature learning and fusion using the PGBM-RBM2 can assist in the diagnosis of PCa. This deep network is expected to be useful in the clinical diagnosis of PCa.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

scholarly journals ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

2019 ◽  
Vol 9 (1) ◽  
pp. 5590-5595
Keyword(s):  
Diabetic Retinopathy ◽  
Research Work ◽  
Texture Features ◽  
Support Vector ◽  
Fundus Images ◽  
Small Vessels ◽  
The Neural Network ◽  
Major Loss ◽  
Occurrence Matrix ◽  
Fluid Leak

The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

scholarly journals Skin Disease Recognition Method Based on Image Color and Texture Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Li-sheng Wei ◽  
Quan Gan ◽  
Tao Ji
Keyword(s):  
Skin Disease ◽  
Skin Diseases ◽  
Texture Features ◽  
Support Vector ◽  
Grey Level ◽  
Recognition Method ◽  
Svm Classification ◽  
Color Features ◽  
Automatic Methods ◽  
Occurrence Matrix

Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

2019 ◽  
Vol 15 (03) ◽  
pp. 47 ◽  
Author(s):  
Ann Nosseir ◽  
Seif Eldin A. Ahmed
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
A Value ◽  
Different Types ◽  
Linear Svm ◽  
Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively.  These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying.  To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

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.

Monitoring Volcanic Activity of Baekdu Mountain based on Ice Area Changes During the Winters of 2015-2020

2021 ◽  
Author(s):  
Arip Syaripudin Nur ◽  
Sungjae Park ◽  
Seulki Lee ◽  
Chang-Wook Lee
Keyword(s):  
Vertical Transmission ◽  
Air Temperature ◽  
Volcanic Activity ◽  
Texture Features ◽  
Support Vector ◽  
Covered Area ◽  
Svm Algorithm ◽  
Highly Correlated ◽  
Occurrence Matrix ◽  
The Relationship

<p>Baekdu Mountain is a 2,744 m high stratovolcano, located on the border of China and North Korea. The mountain has a caldera lake, Lake Cheonji, as a result of past volcanic activity. The ice area changes during winter in Lake Cheonji could act as a proxy for volcanic activity monitoring in Baekdu. As Baekdu laid on a political border, remote sensing allows us to quantify attributes of otherwise inaccessible or dangerous places. We assessed changes in winter (October–April) ice area in a high-altitude groundwater-fed caldera lake using Sentinel-1 synthetic aperture radar (SAR) data acquired from 2015 to 2020. To calculate the ice-covered area, 10 gray level co-occurrence matrix (GLCM) texture features were computed from SAR images obtained with VH (vertical transmission and horizontal reception) and VV (vertical transmission and vertical reception) polarizations. A support vector machine (SVM) algorithm was used to classify ice and water pixels from the GLCM layers, and the results from VH and VV imagery were combined to calculate the total area covered by ice. We examined the relationship between ice area and air temperature from the closest weather station, Samjiyeon using fixed period regression. The ice area was inversely proportional to 30-day averaged air temperature and these variables were highly correlated (-0.86). Our results show that there were no significant ice changes during the period, which indicates that there was no significant volcanic activity in Baekdu Mountain during the winters of 2015–2020. This study is expected to be useful for a better understanding of whether and how ice area changes in volcano lakes aid in eruption forecasting.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document