Hong Chang Tan, Elizabeth Shumbayawonda, Cayden Beyer, Lionel Tim-Ee Cheng, Albert Low, Chin Hong Lim, Alvin Eng, Weng Hoong Chan, Phong Ching Lee, Mei Fang Tay, Stella Kin, Jason Pik Eu Chang, Yong Mong Bee, George Boon Bee Goh
{"title":"Multiparametric Magnetic Resonance Imaging and Magnetic Resonance Elastography to Evaluate the Early Effects of Bariatric Surgery on Nonalcoholic Fatty Liver Disease.","authors":"Hong Chang Tan, Elizabeth Shumbayawonda, Cayden Beyer, Lionel Tim-Ee Cheng, Albert Low, Chin Hong Lim, Alvin Eng, Weng Hoong Chan, Phong Ching Lee, Mei Fang Tay, Stella Kin, Jason Pik Eu Chang, Yong Mong Bee, George Boon Bee Goh","doi":"10.1155/2023/4228321","DOIUrl":"https://doi.org/10.1155/2023/4228321","url":null,"abstract":"<p><strong>Background: </strong>Bariatric surgery is the most effective treatment for morbid obesity and reduces the severity of nonalcoholic fatty liver disease (NAFLD) in the long term. Less is known about the effects of bariatric surgery on liver fat, inflammation, and fibrosis during the early stages following bariatric surgery.</p><p><strong>Aims: </strong>This exploratory study utilises advanced imaging methods to investigate NAFLD and fibrosis changes during the early metabolic transitional period following bariatric surgery.</p><p><strong>Methods: </strong>Nine participants with morbid obesity underwent sleeve gastrectomy. Multiparametric MRI (mpMRI) and magnetic resonance elastography (MRE) were performed at baseline, during the immediate (1 month), and late (6 months) postsurgery period. Liver fat was measured using proton density fat fraction (PDFF), disease activity using iron-correct T1 (cT1), and liver stiffness using MRE. Repeated measured ANOVA was used to assess longitudinal changes and Dunnett's method for multiple comparisons.</p><p><strong>Results: </strong>All participants (Age 45.1 ± 9.0 years, BMI 39.7 ± 5.3 kg/m<sup>2</sup>) had elevated hepatic steatosis at baseline (PDFF >5%). In the immediate postsurgery period, PDFF decreased significantly from 14.1 ± 7.4% to 8.9 ± 4.4% (<i>p</i> = 0.016) and cT1 from 826.9 ± 80.6 ms to 768.4 ± 50.9 ms (<i>p</i> = 0.047). These improvements continued to the later postsurgery period. Bariatric surgery did not reduce liver stiffness measurements.</p><p><strong>Conclusion: </strong>Our findings support using MRI as a noninvasive tool to monitor NAFLD in patient with morbid obesity during the early stages following bariatric surgery.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"4228321"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9919473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Otman Sarrhini, Pedro D'Orléans-Juste, Jacques A Rousseau, Jean-François Beaudoin, Roger Lecomte
{"title":"Enhanced Extraction of Blood and Tissue Time-Activity Curves in Cardiac Mouse FDG PET Imaging by Means of Constrained Nonnegative Matrix Factorization.","authors":"Otman Sarrhini, Pedro D'Orléans-Juste, Jacques A Rousseau, Jean-François Beaudoin, Roger Lecomte","doi":"10.1155/2023/5366733","DOIUrl":"https://doi.org/10.1155/2023/5366733","url":null,"abstract":"<p><p>We propose an enhanced method to accurately retrieve time-activity curves (TACs) of blood and tissue from dynamic 2-deoxy-2-[<sup>18</sup>F]fluoro-D-glucose ([<sup>18</sup>F]FDG) positron emission tomography (PET) cardiac images of mice. The method is noninvasive and consists of using a constrained nonnegative matrix factorization algorithm (CNMF) applied to the matrix (<i>A</i>) containing the intensity values of the voxels of the left ventricle (LV) PET image. CNMF factorizes <i>A</i> into nonnegative matrices <i>H</i> and <i>W</i>, respectively, representing the physiological factors (blood and tissue) and their associated weights, by minimizing an extended cost function. We verified our method on 32 C57BL/6 mice, 14 of them with acute myocardial infarction (AMI). With CNMF, we could break down the mouse LV into myocardial and blood pool images. Their corresponding TACs were used in kinetic modeling to readily determine the [<sup>18</sup>F]FDG influx constant (<i>K</i><sub><i>i</i></sub>) required to compute the myocardial metabolic rate of glucose. The calculated <i>K</i><sub><i>i</i></sub> values using CNMF for the heart of control mice were in good agreement with those published in the literature. Significant differences in <i>K</i><sub><i>i</i></sub> values for the heart of control and AMI mice were found using CNMF. The values of the elements of <i>W</i> agreed well with the LV structural changes induced by ligation of the left coronary artery. CNMF was compared with the recently published method based on robust unmixing of dynamic sequences using regions of interest (RUDUR). A clear improvement of signal separation was observed with CNMF compared to the RUDUR method.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"5366733"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9716473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thore Dietrich, Stephan Theodor Bujak, Thorsten Keller, Bernhard Schnackenburg, Riad Bourayou, Rolf Gebker, Kristof Graf, Eckart Fleck
{"title":"In Vivo Fluorine Imaging Using 1.5 Tesla MRI for Depiction of Experimental Myocarditis in a Rodent Animal Model.","authors":"Thore Dietrich, Stephan Theodor Bujak, Thorsten Keller, Bernhard Schnackenburg, Riad Bourayou, Rolf Gebker, Kristof Graf, Eckart Fleck","doi":"10.1155/2023/4659041","DOIUrl":"https://doi.org/10.1155/2023/4659041","url":null,"abstract":"<p><p>The usefulness of perfluorocarbon nanoemulsions for the imaging of experimental myocarditis has been demonstrated in a high-field 9.4 Tesla MRI scanner. Our proof-of-concept study investigated the imaging capacity of PFC-based <sup>19</sup>F/<sup>1</sup>H MRI in an animal myocarditis model using a clinical field strength of 1.5 Tesla. To induce experimental myocarditis, five male rats (weight ~300 g, age ~50 days) were treated with one application per week of doxorubicin (2 mg/kg BW) over a period of six weeks. Three control animals received the identical volume of sodium chloride 0.9% instead. Following week six, all animals received a single 4 ml injection of an 20% oil-in-water perfluorooctylbromide nanoemulsion 24 hours prior to <i>in vivo</i><sup>1</sup>H/<sup>19</sup>F imaging on a 1.5 Tesla MRI. After euthanasia, cardiac histology and immunohistochemistry using CD68/ED1 macrophage antibodies were performed, measuring the inflamed myocardium in <i>μ</i>m<sup>2</sup> for further statistical analysis to compare the extent of the inflammation with the <sup>19</sup>F-MRI signal intensity. All animals treated with doxorubicin showed a specific signal in the myocardium, while no myocardial signal could be detected in the control group. Additionally, the doxorubicin group showed a significantly higher SNR for <sup>19</sup>F and a stronger CD68/ED1 immunhistoreactivity compared to the control group. This proof-of-concept study demonstrates that perfluorocarbon nanoemulsions could be detected in an <i>in vivo</i> experimental myocarditis model at a currently clinically relevant field strength.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2023 ","pages":"4659041"},"PeriodicalIF":7.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9855524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.","authors":"Rumana Islam, Mohammed Tarique","doi":"10.1155/2022/5318447","DOIUrl":"10.1155/2022/5318447","url":null,"abstract":"<p><p>This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2022 ","pages":"5318447"},"PeriodicalIF":3.3,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10464881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seyyed M H Haddad, Christopher J M Scott, Miracle Ozzoude, Courtney Berezuk, Melissa Holmes, Sabrina Adamo, Joel Ramirez, Stephen R Arnott, Nuwan D Nanayakkara, Malcolm Binns, Derek Beaton, Wendy Lou, Kelly Sunderland, Sujeevini Sujanthan, Jane Lawrence, Donna Kwan, Brian Tan, Leanne Casaubon, Jennifer Mandzia, Demetrios Sahlas, Gustavo Saposnik, Ayman Hassan, Brian Levine, Paula McLaughlin, J B Orange, Angela Roberts, Angela Troyer, Sandra E Black, Dar Dowlatshahi, Stephen C Strother, Richard H Swartz, Sean Symons, Manuel Montero-Odasso, Ondri Investigators, Robert Bartha
{"title":"Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity.","authors":"Seyyed M H Haddad, Christopher J M Scott, Miracle Ozzoude, Courtney Berezuk, Melissa Holmes, Sabrina Adamo, Joel Ramirez, Stephen R Arnott, Nuwan D Nanayakkara, Malcolm Binns, Derek Beaton, Wendy Lou, Kelly Sunderland, Sujeevini Sujanthan, Jane Lawrence, Donna Kwan, Brian Tan, Leanne Casaubon, Jennifer Mandzia, Demetrios Sahlas, Gustavo Saposnik, Ayman Hassan, Brian Levine, Paula McLaughlin, J B Orange, Angela Roberts, Angela Troyer, Sandra E Black, Dar Dowlatshahi, Stephen C Strother, Richard H Swartz, Sean Symons, Manuel Montero-Odasso, Ondri Investigators, Robert Bartha","doi":"10.1155/2022/5860364","DOIUrl":"https://doi.org/10.1155/2022/5860364","url":null,"abstract":"<p><p>Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T<sub>1</sub>-weighted, proton density-weighted, T<sub>2</sub>-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"5860364"},"PeriodicalIF":7.6,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40445853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model.","authors":"Abdelmajid Bousselham, Omar Bouattane, Mohamed Youssfi, Abdelhadi Raihani","doi":"10.1155/2022/5529726","DOIUrl":"https://doi.org/10.1155/2022/5529726","url":null,"abstract":"<p><p>Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using <i>k</i>-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"5529726"},"PeriodicalIF":7.6,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40648868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Raeymaeckers, Yannick De Brucker, Maurizio Tosi, N. Buls, J. Mey
{"title":"Relative Perfusion Differences between Parathyroid Adenomas and the Thyroid on Multiphase 4DCT","authors":"S. Raeymaeckers, Yannick De Brucker, Maurizio Tosi, N. Buls, J. Mey","doi":"10.1155/2022/2984789","DOIUrl":"https://doi.org/10.1155/2022/2984789","url":null,"abstract":"A multiphase 4DCT technique can be useful for the detection of parathyroid adenomas. Up to 16 different phases can be obtained without significant increase of exposure dose using wide beam axial scanning. This technique also allows for the calculation of perfusion parameters in suspected lesions. We present data on 19 patients with histologically proven parathyroid adenomas. We find a strong correlation between 2 perfusion parameters when comparing parathyroid adenomas and thyroid tissue: parathyroid adenomas show a 55% increase in blood flow (BF) (p < 0.001) and a 50% increase in blood volume (BV) (p < 0.001) as compared to normal thyroid tissue. The analysis of the ROC curve for the different perfusion parameters demonstrates a significantly high area under the curve for BF and BV, confirming these two perfusion parameters to be a possible discriminating tool to discern between parathyroid adenomas and thyroid tissue. These findings can help to discern parathyroid from thyroid tissue and may aid in the detection of parathyroid adenomas.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47701953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform","authors":"W. He, Linman Zhao","doi":"10.1155/2022/7251674","DOIUrl":"https://doi.org/10.1155/2022/7251674","url":null,"abstract":"The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49417825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal
{"title":"Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization","authors":"Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal","doi":"10.1155/2022/3211793","DOIUrl":"https://doi.org/10.1155/2022/3211793","url":null,"abstract":"In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2022 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44197574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan
{"title":"Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image","authors":"R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan","doi":"10.1155/2022/5336373","DOIUrl":"https://doi.org/10.1155/2022/5336373","url":null,"abstract":"Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44414554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}