Image classification of vaginal microecology detection based on gabor texture and LSTM model

2021 ◽  
pp. 1-18
Author(s):  
Gaoteng Yuan ◽  
Yinping Dong ◽  
Xiaofeng Zhou
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Short Term Memory ◽  
Imbalanced Data ◽  
Region Of Interest ◽  
Texture Features ◽  
Image Features ◽  
Imbalanced Data Sets ◽  
Area Of Interest ◽  
Gynecological Diseases

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

scholarly journals MRI in prostate cancer radiology: what is currently known?

2021 ◽  
Author(s):  
Pavel Gelezhe ◽  
Andreevich I. Blokhin ◽  
Serafim Semenov ◽  
Damiano Карузо
Keyword(s):  
Prostate Cancer ◽  
Texture Analysis ◽  
Treatment Options ◽  
Texture Features ◽  
Future Research ◽  
Textural Features ◽  
Mathematical Methods ◽  
Additional Information ◽  
Area Of Interest ◽  
Diagnostic Potential

Approaches to the diagnosis and treatment of prostate cancer rely on a combination of magnetic resonance imaging (MRI) and histological data. The purpose of this review is to introduce the reader to the basics of the current diagnostic approach to prostate cancer with a focus on texture analysis (TA). Texture analysis allows the evaluation of relationships between image pixels using mathematical methods, which provides additional information. First-order texture analysis of features can have greater clinical reproducibility than higher-order texture features. Textural features extracted from diffusion coefficient maps have shown the greatest clinical relevance. Future research should focus on integrating machine learning methods to facilitate the use of texture analysis in clinical practice. Development of automated segmentation methods is required to reduce the likelihood of including normal tissue in the area of interest. Texture analysis allows noninvasive separation of patients into groups in terms of possible treatment options. Currently, there are few clinical studies on the differential diagnosis of clinically significant prostate cancer, including Gleason and ISUP grading. Large prospective studies are required to verify the diagnostic potential of textural features.

scholarly journals Scanpath Prediction for Visual Attention using IOR-ROI LSTM

2018 ◽  
Author(s):  
Zhenzhong Chen ◽  
Wanjie Sun
Keyword(s):  
Visual Attention ◽  
Short Term Memory ◽  
Region Of Interest ◽  
Image Features ◽  
Superior Performance ◽  
Gaze Shift ◽  
Shift Behavior ◽  
Proposed Model ◽  
Scene Information ◽  
Core Part

Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.

scholarly journals Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

2022 ◽  
Vol 23 (2) ◽  
pp. 637
Author(s):  
Filippo Crimì ◽  
Emilio Quaia ◽  
Giulio Cabrelle ◽  
Chiara Zanon ◽  
Alessia Pepe ◽  
...  
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Texture Analysis ◽  
Adrenal Adenoma ◽  
Region Of Interest ◽  
Hounsfield Unit ◽  
Texture Features ◽  
Baseline Evaluation ◽  
Adrenocortical Carcinomas ◽  
Histological Confirmation

Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.

scholarly journals Image Aesthetic Assessment Based on Image Classification and Region Segmentation

2020 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Quyet-Tien Le ◽  
Patricia Ladret ◽  
Huu-Tuan Nguyen ◽  
Alice Caplier
Keyword(s):  
Image Classification ◽  
Region Of Interest ◽  
Image Features ◽  
Regions Of Interest ◽  
Large Field ◽  
Region Segmentation ◽  
Learned Features

The main goal of this paper is to study Image Aesthetic Assessment (IAA) indicating images as high or low aesthetic. The main contributions concern three points. Firstly, following the idea that photos in different categories (human, flower, animal, landscape, …) are taken with different photographic rules, image aesthetic should be evaluated in a different way for each image category. Large field images and close-up images are two typical categories of images with opposite photographic rules so we want to investigate the intuition that prior Large field/Close-up Image Classification (LCIC) might improve the performance of IAA. Secondly, when a viewer looks at a photo, some regions receive more attention than other regions. Those regions are defined as Regions Of Interest (ROI) and it might be worthy to identify those regions before IAA. The question “Is it worthy to extract some ROIs before IAA?” is considered by studying Region Of Interest Extraction (ROIE) before investigating IAA based on each feature set (global image features, ROI features and background features). Based on the answers, a new IAA model is proposed. The last point is about a comparison between the efficiency of handcrafted and learned features for the purpose of IAA.

scholarly journals Explainable information retrieval using deep learning for medical images

2021 ◽  
pp. 49-49
Author(s):  
Apoorva Singh ◽  
Husanbir Pannu ◽  
Avleen Malhi
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Real Life ◽  
Region Of Interest ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Bleeding Detection ◽  
Real Life Situation ◽  
The Impact

Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Ex plainable Artificial Intelligence (XAI) module has been utilized to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods.

scholarly journals Plant Disease Identification Using Discrete Wavelet Transforms and SVM

2021 ◽  
Vol 23 (06) ◽  
pp. 108-112
Author(s):  
Kiran S M ◽  
◽  
Dr. Chandrappa D N ◽  
Keyword(s):  
Wavelet Transforms ◽  
Region Of Interest ◽  
Texture Features ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Classifier Performance ◽  
Sensitivity Specificity

Disease detection in plants is one of the essential steps in the field of agriculture to improve the quality and yield of crops. Applications of image processing play a major role in the early detection of diseases and also in terms of accuracy and time consumption. In many plant health monitoring systems, Fourier and wavelet transform is applied for feature extraction from plant images and then they are classified using different classifiers. In this study, tomato leaf images are collected from the PlantVillage database, images are preprocessed to improve the contrast, and then image segmentation is performed using the k-means clustering technique. Texture features are extracted from the region of interest using Discrete Wavelet Transforms (DWT). Fourteen image features obtained from Daubechies (db3), Symlet (sym3), and biorthogonal (Bior 3.3, Bior 3.5, Bior 3.7) wavelets. These features are used to classify the images as healthy and unhealthy with the help of the Support Vector Machine (SVM) classifier. Performance of the system is measured in terms of Sensitivity, Specificity, and Accuracy. The proposed system classifies the images with a sensitivity of 92%, specificity of 84%, and accuracy of 88%.

scholarly journals Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaonan Mao ◽  
Yan Guo ◽  
Feng Wen ◽  
Hongyuan Liang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Texture Analysis ◽  
Transarterial Chemoembolization ◽  
Tumor Response ◽  
Region Of Interest ◽  
Texture Features ◽  
Discriminative Performance ◽  
Arterial Enhancement ◽  
Enhanced Computed Tomography

Abstract Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals T1 mapping-derived signature of myocardial involvement in idiopathic inflammatory myopathy compared to acute viral myocarditis: a texture-based analysis

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Bollache ◽  
AT Huber ◽  
J Lamy ◽  
E Afari ◽  
TM Bacoyannis ◽  
...  
Keyword(s):  
Contrast Agent ◽  
Texture Analysis ◽  
T1 Mapping ◽  
Inflammatory Myopathy ◽  
Viral Myocarditis ◽  
Texture Features ◽  
Idiopathic Inflammatory Myopathy ◽  
Post Contrast ◽  
Myocardial T1 ◽  
Myocardial Involvement

Abstract Funding Acknowledgements Type of funding sources: None. Background. Recent studies revealed the ability of MRI T1 mapping to characterize myocardial involvement in both idiopathic inflammatory myopathy (IIM) and acute viral myocarditis (AVM), as compared to healthy controls. However, neither myocardial T1 nor T2 maps were able to discriminate between IIM and AVM patients, when considering conventional myocardial mean values and derived indices such as lambda and extracellular volume. Purpose. To investigate the ability of T1 mapping-derived texture analysis to differentiate IIM from AVM. Methods. Forty patients, 20 with IIM (51 ± 17 years, 9 men) and 20 with AVM (34 ± 13 years, 16 men) underwent 1.5T MRI T1 mapping using a modified Look-Locker inversion-recovery sequence before and 15 minutes after injection of a gadolinium contrast agent. After manual delineation of endocardial and epicardial borders and co-registration of all inversion time images, native and post-contrast T1 maps were estimated. Myocardial texture analysis was performed on native T1 maps. Textural features such as: autocorrelation, contrast, dissimilarity, energy and sum entropy were used to build a least squares-based linear regression model. Finally, receiver operating characteristic (ROC) analysis was used to investigate the ability of such texture features score to classify IIM vs. AVM patients, compared to the performance of mean myocardial T1. A Wilcoxon rank-sum test was also used to test difference significance between groups. Results. Both native and post-contrast mean myocardial T1 values were comparable between IIM (native: 1022 ± 43 ms; post-contrast: 319 ± 44 ms) and AVM (1056 ± 59 ms, p = 0.07; 318 ± 35 ms, p = 0.90, respectively) groups. Results of ROC analyses are provided in the Table, indicating that a better discrimination between IIM and AVM patients was obtained when using texture features, with higher AUC and accuracy than mean T1 values (Figure). Conclusion. Texture analysis derived from MRI T1 maps without contrast agent injection was able to discriminate between IIM and AVM with higher accuracy, sensitivity and specificity than conventional T1 indices. Such analysis could provide a useful myocardial signature to help diagnose and manage cardiac alterations associated with IIM in patients presenting with myocarditis and primarily suspected of AVM. Table Area under curve (AUC) Accuracy Sensitivity Specificity Native T1 0.67 0.70 0.65 0.75 Post-contrast T1 0.49 0.60 0.25 0.95 Texture features score 0.85 0.82 0.90 0.75 ROC analyses for classification between IIM and AVM patients Abstract Figure

scholarly journals MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer

2021 ◽  
Vol 10 (2) ◽  
pp. 237
Author(s):  
Jung Hyun Park ◽  
Byung Se Choi ◽  
Jung Ho Han ◽  
Chae-Yong Kim ◽  
Jungheum Cho ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Brain Metastases ◽  
Stereotactic Radiosurgery ◽  
Texture Analysis ◽  
Multivariable Analysis ◽  
Texture Features ◽  
Progression Free Survival ◽  
Clinical Parameter ◽  
Imaging Biomarker ◽  
Local Progression

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

Sign in / Sign up

Export Citation Format

Share Document