The Clinical Value of Ultrasound Image Texture Analysis in the Diagnosis of Uterine Adhesions

Purpose: This review examines the diagnostic value of transvaginal 3D ultrasound image texture analysis for the diagnosis of uterine adhesions. Materials and Methods: The total clinical data of 53 patients with uterine adhesions diagnosed by hysteroscopy and the imaging data of transvaginal three-dimensional ultrasound from the Second Affiliated Hospital of Chongqing Medical University from June 2022 to August 2023 were retrospectively analysed. Based on hysteroscopic surgical records, patients were divided into two independent groups: normal endometrium and uterine adhesion sites. The samples were divided into a training set and a test set, and the transvaginal 3D ultrasound was used to outline the region of interest (ROI) and extract texture features for normal endometrium and uterine adhesions based on hysteroscopic surgical recordings, the training set data were feature screened and modelled using lasso regression and cross-validation, and the diagnostic efficacy of the model was assessed by applying the subjects’ operating characteristic (ROC) curves. Results: For each group, 290 texture feature parameters were extracted and three higher values were screened out, and the area under the curve of the constructed ultrasonographic scoring model was 0.658 and 0.720 in the training and test sets, respectively. Conclusion Relative clinical value of transvaginal three-dimensional ultrasound image texture analysis for the diagnosis of uterine adhesions.

Real-time ore sorting using color and texture analysis

Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.

Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula

BACKGROUND Perianal fistulising Crohn's disease(PFCD)and glandular anal fistula have many similarities on conventional magnetic resonance imaging.However,many patients with PFCD show concomitant active proctitis,but only few patients with glandular anal fistula have active proctitis.AIM To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging(FS-T2WI).METHODS Patients with rectal water sac implantation were screened from the first part of this study(48 patients with PFCD and 22 patients with glandular anal fistula).Open-source software ITK-SNAP(Version 3.6.0,http://www.itksnap.org/)was used to delineate the region of interest(ROI)of the entire rectum and anal canal wall on every axial section,and then the ROIs were input in the Analysis Kit software(version V3.0.0.R,GE Healthcare)to calculate the textural feature parameters.Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test.The redundant textural parameters were screened by bivariate Spearman correlation analysis,and binary logistic regression analysis was used to establish the model of textural feature parameters.Finally,diagnostic accuracy was assessed by receiver operating characteristic-area under the curve(AUC)analysis.RESULTS In all,385 textural parameters were obtained,including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups.Then,16 texture feature parameters remained after bivariate Spearman correlation analysis,including one histogram parameter(Histogram energy);four grey level co-occurrence matrix(GLCM)parameters(GLCM energy_all direction_offset1_SD,GLCM entropy_all direction_offset4_SD,GLCM entropy_all direction_offset7_SD,and Haralick correlation_all direction_offset7_SD);four texture parameters(Correlation_all direction_offset1_SD,cluster prominence_angle 90_offset4,Inertia_all direction_offset7_SD,and cluster shade_angle 45_offset7);five grey level run-length matrix parameters(grey level nonuniformity_angle 90_offset1,grey level nonuniformity_all direction_offset4_SD,long run high grey level emphasis_all direction_offset1_SD,long run emphasis_all direction_offset4_SD,and long run high grey level emphasis_all direction_offset4_SD);and two form factor parameters(surface area and maximum 3D diameter).The AUC,sensitivity,and specificity of the model of textural feature parameters were 0.917,85.42%,and 86.36%,respectively.CONCLUSION The model of textural feature parameters showed good diagnostic performance for PFCD.The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.

The utility of two-dimensional shear wave elastography and texture analysis for monitoring liver fibrosis in rat model

Background:Liver fibrosis is a common pathological change caused by a variety of etiologies.Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis.This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model.Methods:Twenty rats were divided into control group(n=4)and experimental groups(n=4 per group)with carbon tetrachloride administration for 2,3,4,and 6 weeks.The liver stiffness measurement was performed by two-dimensional shear wave elastography,while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda.The results of elastography and texture analysis were validated through comparing with histopathology.Results:Liver stiffness measurement was 6.09±0.31 kPa in the control group and 7.10±0.41 kPa,7.80±0.93 kPa,8.64±0.93 kPa,9.91±1.13 kPa in the carbon tetrachloride induced groups for 2,3,4,6 weeks,respectively(P<0.05).By texture analysis,histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis.Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0%and 92.5%to discriminate liver fibrosis and non-fibrosis,respectively.In texture analysis,five optimal parameters were selected to classify liver fibrosis and non-fibrosis.Conclusions:Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis,even in mild fibrosis.Texture analysis can further extract and quantify the texture features in ultrasonic image,which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma

Objective:The objective of this study was to investigate the application of unenhanced computed tomography(CT)texture analysis in differentiating pancreatic adenosquamous carcinoma(PASC)from pancreatic ductal adenocarcinoma(PDAC).Methods:Preoperative CT images of 112 patients(31 with PASC,81 with PDAC)were retrospectively reviewed.A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis.Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test,univariate logistic regression analysis,and the minimum redundancy maximum relevance algorithm.Furthermore,receiver operating characteristic(ROC)curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest(RF)method.Finally,the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation(LGOCV)method.

CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer

Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes.The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer(CRC)patients using CT texture analysis.Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations.The patients were assigned into a training cohort(n=51)and a validation cohort(n=41).We placed the region of interest in the tumour regions on the selected axial images using software of lexRad to extract a series of quantitative parameters based on the spatial scaling factors(SSFs),including mean,standard deviation(SD),entropy,mean of positive pixels(MPP),skewness,and kurtosis.The texture parameters and clinical characteristics(age,gender,tumour location,histopathology;tumour size,1 N,M stages)were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort.Before building the multiple feature classifier,we calculated the correlations of the features using Pearsons correlation coefficient,and if any two features were significantly correlated,the one with lower AUC was removed.Ultimately,only the most discriminative isolated features were combined to train a supporting vector machine(SVM)classifier.The receiver operating characteristic(ROC)curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS.Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts.For predicting the expression of mutated KRAS in CRC patients,the perfect model which combined skewness on SSF 5 by unenhanced CT,entropy on SSF 2,skewness and kurtosis on SSF 0,and kurtosis and mean on SSF 3 by enhanced CT,showed a desirable AUC of 0.951(95%CI:0.895-1,P<0.001),with a sensitivity of 88.9%and a specificity of 91.7%,when the cut-off value was 0.46 in the training cohort;while in the validation cohort,the AUC value was 0.995(95%CI:0.982-1,P<0.001),the sensitivity was 100%,and the specificity was 93.7%when the cut-off value was 0.28.Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation

Background:Radiofrequency ablation(RFA)is one of the effective therapeutic modalities in patients with hepatocellular carcinoma(HCC).However,there is no proper method to evaluate the HCC response to RFA.This study aimed to establish and validate a clinical prediction model based on dual-energy com-puted tomography(DECT)quantitative-imaging parameters,clinical variables,and CT texture parameters.Methods:We enrolled 63 patients with small HCC.Two to four weeks after RFA,we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients’clinical baseline variables.DECT images were manually segmented,and 56 CT texture features were extracted.We used LASSO al-gorithm for feature selection and data dimensionality reduction;logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters;we then added texture features to build a clinical-texture model based on clinical model.Results:A total of six optimal CT texture analysis(CTTA)features were selected,which were statis-tically different between patients with or without tumor progression(P<0.05).When clinical vari-ables and DECT-quantitative parameters were included,the clinical models showed that albumin-bilirubin grade(ALBI)[odds ratio(OR)=2.77,95%confidence interval(CI):1.35-6.65,P=0.010],λAP(40-100 keV)(OR=3.21,95%CI:3.16-5.65,P=0.045)and IC AP(OR=1.25,95%CI:1.01-1.62,P=0.028)were asso-ciated with tumor progression,while the clinical-texture models showed that ALBI(OR=2.40,95%CI:1.19-5.68,P=0.024),λAP(40-100 keV)(OR=1.43,95%CI:1.10-2.07,P=0.019),and CTTA-score(OR=2.98,95%CI:1.68-6.66,P=0.001)were independent risk factors for tumor progression.The clinical model,clinical-texture model,and CTTA-score all performed well in predicting tumor progression within 12 months after RFA(AUC=0.917,0.962,and 0.906,respectively),and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957,respectively.Conclusions:DECT-quantitative parameters,CTTA,and clinical variables were helpful in predicting HCC progression after RFA.The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases,however,the results have varied.Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated.This study was designed to determine if the computed tomography(CT)texture analysis results of non-necrotic colorectal liver metastases differ from previous reports.A larger range of texture features were also evaluated to identify potential new biomarkers.AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases(CRLMs).METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice.The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm(diagnosed on archived 1.25 mm portal venous phase CT(FOLFOX,FOLFIRI,FOLFOXIRI,CAPE-OX,CAPE-IRI or capecitabine).The final study cohort consisted of 29 patients.The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria.By means of CT texture analysis,various first and second order texture features were extracted from a single nonnecrotic target CRLM in each responding and non-responding patient.Associations between features and response to chemotherapy were assessed by logistic regression models.The prognostic accuracy of selected features was evaluated by using the area under the curve.RESULTS There were 15 responders(partial response)and 14 non-responders(7 stable and 7 with progressive disease).The responders presented with a higher number of CRLMs(P=0.05).In univariable analysis,eight texture features of the responding CRLMs were associated with treatment response,but due to strong correlations among some of the features,only two features,namely minimum histogram gradient intensity and long run low grey level emphasis,were included in the multiple analysis.The area under the receiver operating characteristic curve of the multiple model was 0.80(95%CI:0.64 to 0.96),with a sensitivity of 0.73(95%CI:0.48 to 0.89)and a specificity of 0.79(95%CI:0.52 to 0.92).CONCLUSION Eight first and second order texture features,but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

Determination of Rennet Clotting Time by Texture Analysis Method

In this study, texture analysis method was used for the determination of rennet flocculation time (tfloc) and rennet clotting time (tclot) of rennet-induced reconstitued milk samples with different CaCl2 concentrations. The rennet flocculation time (RFT) and rennet clotting time (RCT) were also determined by using the Berridge test and sensory evaluation. The hardness value versus renneting time curves derived from texture analysis gave a good modified exponential relationship for each CaCl2 concentration and the curves were used to calculate flocculation time and clotting time parameters. It was found that the parameters (tfloc and tclot) appeared strongly correlated with RFT and RCT, respectively. Texture analysis was proved as a suitable method to control the rennet-induced coagulation and determine the rennet clotting time. It was also determined that enrichment of milk with CaCl2 leaded to a decrease in flocculation and clotting times and an increase in rate of clotting and gel hardness.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma:Retrospective study

BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance(MR)imaging(MRI)in predicting microvascular invasion(MVI)in preoperative HCC.METHODS This study included 105 patients with pathologically confirmed HCC,categorized into MVI-positive and MVI-negative groups.We employed Original Data Analysis,Principal Component Analysis,Linear Discriminant Analysis(LDA),and Non-LDA(NDA)for texture analysis using multi-parametric MR images to predict preoperative MVI.The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software,with results expressed as the misjudgment rate(MCR).RESULTS Texture analysis using multi-parametric MRI,particularly the MI+PA+F dimensionality reduction method combined with NDA discrimination,demonstrated the most effective prediction of MVI in HCC.Prediction accuracy in the pulse and equilibrium phases was 83.81%.MCRs for the combination of T2-weighted imaging(T2WI),arterial phase,portal venous phase,and equilibrium phase were 22.86%,16.19%,20.95%,and 20.95%,respectively.The area under the curve for predicting MVI positivity was 0.844,with a sensitivity of 77.19%and specificity of 91.67%.CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI,portal venous,and equilibrium phases.This study provides an objective,non-invasive method for preoperative prediction of MVI,offering a theoretical foundation for the selection of clinical therapy.

Hurricane eye morphology extraction from SAR images by texture analysis

Tropical hurricanes are among the most devastating hazards on Earth.Knowledge about its intense inner-core structure and dynamics will improve hurricane forecasts and advisories.The precise morphological parameters extracted from high-resolution spaceborne Synthetic Aperture Radar(SAR)images,can play an essential role in further exploring and monitoring hurricane dynamics,especially when hurricanes undergo amplification,shearing,eyewall replacements and so forth.Moreover,these parameters can help to build guidelines for wind calibration of the more abundant,but lower resolution scatterometer wind data,thus better linking scatterometer wind fields to hurricane categories.In this paper,we develop a new method for automatically extracting the hurricane eyes from C-band SAR data by constructing Gray Level-Gradient Co-occurrence Matrices(GLGCMs).The hurricane eyewall is determined with a two-dimensional vector,generated by maximizing the class entropy of the hurricane eye region in GLGCM.The results indicate that when the hurricane is weak,or the eyewall is not closed,the hurricane eye extracted with this automatic method still agrees with what is observed visually,and it preserves the texture characteristics of the original image.As compared to Du’s wavelet analysis method and other morphological analysis methods,the approach developed here has reduced artefacts due to factors like hurricane size and has lower programming complexity.In summary,the proposed method provides a new and elegant choice for hurricane eye morphology extraction.

A robust automatic leukocyte recognition method based on island-clustering texture

A leukocyte recognition method for human peripheral blood smear based on island-clustering texture(ICT)is proposed.By analyzing the features of the five typical classes of leukocyte images,a new ICT model is established.Firstly,some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands.Secondly,the growing region is employed to create regions of the islands in which the seeds are just these feature points.These islands distribution can describe a new texture.Finally,a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte.Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3%with a total sample of 1310 leukocytes.Experimental results show the feasibility of the proposed method.Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.

A computer-based image analysis for tear ferning featuring

The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0
and 45
,contrast,and correlation at 0
and 45
;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.

An Unique Glomerophyric Diorite Porphyry from the Southern Margin of North China Craton:Geochronology,Geochemical and Quantitative Textural Analysis Constraints

1 Introduction The Wulong glomerophyric diorite porphyry has an extremely peculiar texture with plagioclase phenocrysts clustered as flower-like glomerocrysts(Figs.1a&b),which is never discovered elsewhere of the world.

Current status and future perspectives of radiomics in hepatocellular carcinoma

Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease,the management of hepatocellular carcinoma(HCC)patients requires experienced multidisciplinary team discussion.Moreover,imaging plays a key role in the diagnosis,staging,restaging,and surveillance of HCC.Currently,imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability.Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging.The main potential applications of radiomic models in HCC are to predict histology,response to treatment,genetic signature,recurrence,and survival.Despite the encouraging results to date,there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice.The purpose of this article is to review the main concepts and challenges pertaining to radiomics,and to review recent studies and potential applications of radiomics in HCC.

Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce the mortality rate.In this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)method.Identifying different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture.Ten thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS.Results prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of specificity.The overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.