Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
10 results
Search Results
Item Numerical analysis of the effect of turbulence transition on the hemodynamic parameters in human coronary arteries(AME Publishing Company info@amepc.org, 2016) Mahalingam, A.; Gawandalkar, U.U.; Kini, G.; Buradi, A.; Araki, T.; Ikeda, N.; Nicolaïdes, A.; Laird, J.R.; Saba, L.; Suri, J.S.Background: Local hemodynamics plays an important role in atherogenesis and the progression of coronary atherosclerosis disease (CAD). The primary biological effect due to blood turbulence is the change in wall shear stress (WSS) on the endothelial cell membrane, while the local oscillatory nature of the blood flow affects the physiological changes in the coronary artery. In coronary arteries, the blood flow Reynolds number ranges from few tens to several hundreds and hence it is generally assumed to be laminar while calculating the WSS calculations. However, the pulsatile blood flow through coronary arteries under stenotic condition could result in transition from laminar to turbulent flow condition. Methods: In the present work, the onset of turbulent transition during pulsatile flow through coronary arteries for varying degree of stenosis (i.e., 0%, 30%, 50% and 70%) is quantitatively analyzed by calculating the turbulent parameters distal to the stenosis. Also, the effect of turbulence transition on hemodynamic parameters such as WSS and oscillatory shear index (OSI) for varying degree of stenosis is quantified. The validated transitional shear stress transport (SST) k-? model used in the present investigation is the best suited Reynolds averaged Navier-Stokes turbulence model to capture the turbulent transition. The arterial wall is assumed to be rigid and the dynamic curvature effect due to myocardial contraction on the blood flow has been neglected. Results: Our observations shows that for stenosis 50% and above, the WSSavg, WSSmax and OSI calculated using turbulence model deviates from laminar by more than 10% and the flow disturbances seems to significantly increase only after 70% stenosis. Our model shows reliability and completely validated. Conclusions: Blood flow through stenosed coronary arteries seems to be turbulent in nature for area stenosis above 70% and the transition to turbulent flow begins from 50% stenosis. © Cardiovascular Diagnosis and Therapy. All rights reserved.Item Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Araki, T.; Kumar, P.K.; Suri, H.S.; Ikeda, N.; Gupta, A.; Saba, L.; Rajan, J.; Lavra, F.; Sharma, A.M.; Shafique, S.; Nicolaïdes, A.; Laird, J.R.; Suri, J.S.The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques. © 2016, Springer Science+Business Media New York.Item Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach(Springer Verlag service@springer.de, 2017) Krishna Kumar, P.; Araki, T.; Rajan, J.; Saba, L.; Lavra, F.; Ikeda, N.; Sharma, A.M.; Shafique, S.; Nicolaïdes, A.; Laird, J.R.; Gupta, A.; Suri, J.S.Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients’ left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system. © 2016, International Federation for Medical and Biological Engineering.Item Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population(Springer London, 2018) Areeckal, A.S.; Jayasheelan, N.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis. © 2017, International Osteoporosis Foundation and National Osteoporosis Foundation.Item Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data(Elsevier Ltd, 2018) Areeckal, A.S.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. © 2018 Elsevier LtdItem A novel deep classifier framework for automated molecular subtyping of breast carcinoma using immunohistochemistry image analysis(Elsevier Ltd, 2022) Mathew, T.; Niyas, S.; Johnpaul, C.I.; Kini, J.; Rajan, J.Breast carcinoma has various subtypes based on the genetic factors involved in the pathogenesis of the malignancy. Identifying the exact subtype and providing targeted treatment to the patient can improve the survival chances. Molecular subtyping through immunohistochemistry analysis is a pathology procedure to determine the subtype of breast cancer. The existing manual procedure is tedious and involves assessing the status of the four vital molecular biomarkers present in the tumor tissues. In this paper, a deep learning-based framework for automated molecular subtyping of breast cancer is proposed. Digital slide images of the four biomarkers are separately processed by the proposed framework. In the preprocessing stage, the non-informative background regions from the images are separated. The patches extracted from the foreground regions are classified into target classes using convolutional neural network models trained for this purpose. Classification results are post-processed to predict the status of all the four biomarkers. The predictions for the individual biomarkers are finally consolidated as per clinical guidelines to determine the subtype of the cancer. The proposed system is evaluated for the performance of individual biomarker status prediction and patient-level subtype classification.For patient-level evaluation of biomarkers ER, PR, K67, and HER2, the proposed method gives F1 Scores 1.00, 1.00, 0.90, and 0.94 respectively, whereas for molecular subtyping an F1 score of 0.89 is obtained. In both these aspects, the proposed framework has given significant results that show the effectiveness of our approach. © 2022 Elsevier LtdItem Semi-supervised structure attentive temporal mixup coherence for medical image segmentation(Elsevier B.V., 2022) Pawan, S.J.; Jeevan, G.; Rajan, J.Deep convolutional neural networks have shown eminent performance in medical image segmentation in supervised learning. However, this success is predicated on the availability of large volumes of pixel-level labeled data, making these approaches impractical when labeled data is scarce. On the other hand, semi-supervised learning utilizes pertinent information from unlabeled data along with minimal labeled data, alleviating the demand for labeled data. In this paper, we leverage the mixup-based risk minimization operator in a student–teacher-based semi-supervised paradigm along with structure-aware constraints to enforce consistency coherence among the student predictions for unlabeled samples and the teacher predictions for the corresponding mixup sample by significantly diminishing the need for labeled data. Besides, due to the intrinsic simplicity of the linear combination operation used for generating mixup samples, the proposed method stands at a computational advantage over existing consistency regularization-based SSL methods. We experimentally validate the performance of the proposed model on two public benchmark datasets, namely the Left Atrial (LA) and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. Notably, on the LA dataset's lowest labeled data set-up (5%), the proposed method significantly improved the Dice Similarity Coefficient and the Jaccard Similarity Coefficient by 1.08% and 1.46%, respectively. Furthermore, we demonstrate the efficacy of the proposed method with a consistent improvement across various labeled data proportions on the aforementioned datasets. © 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesItem AAPFC-BUSnet: Hierarchical encoder–decoder based CNN with attention aggregation pyramid feature clustering for breast ultrasound image lesion segmentation(Elsevier Ltd, 2024) Sushma, B.; Pulikala, A.Breast cancer causes a serious menace to women's health and lives, underscoring the urgency of accurate tumor detection. Detecting both cancerous and non-cancerous breast tumors has become increasingly crucial, with ultrasound imaging emerging as a widely adopted modality for this purpose. However, identifying breast lesions in ultrasound images is a challenging task due to various tumor morphologies, geometry, similar color intensity distributions, and fuzzy boundaries, particularly irregularly shaped malignant tumors. This work proposes an encoder–decoder based U-shaped convolutional neural network (CNN) variant with an attention aggregation-based pyramid feature clustering module (AAPFC) to detect breast lesion regions. The network consists of the U-Net variant as a base network and AAPFC to fuse features extracted at the various levels of the base U-Net using a suitable feature fusion technique. Furthermore, the deformable convolution with adaptive self-attention mechanism is introduced to decode the pyramid features parallel to capture the various geometric features at multi-stages. Two public breast lesion ultrasound datasets consisting 263 malignant, 547 benign and 133 normal images are considered to evaluate the performance of the proposed model and state-of-the-art deep CNN-based segmentation models. The proposed model provides 96% accuracy, 68% Mean-IoU, 97% specificity, 82% sensitivity and 0.747 kappa score respectively. The conducted qualitative and quantitative performance analysis experiments show that the proposed model performs better in breast lesion segmentation on ultrasound images. © 2024 Elsevier LtdItem The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise(Springer Science and Business Media Deutschland GmbH, 2024) Hegde, A.; Vijayasenan, D.; Mandava, P.; Menon, G.Machine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, validate and deploy a prognostic model for Intracerebral Haemorrhage (ICH). The data of patients admitted with Spontaneous Intracerebral haemorrhage from January 2015 to December 2019 was accessed from our prospectively maintained hospital stroke registry. 80% of the dataset was used for training, 10% for validation, and 10% for testing. Seventeen input variables were used to predict the dichotomized outcomes (Good outcome mRS 0–3/ Bad outcome mRS 4–6), using machine learning (ML) and logistic regression (LR) models. The two different approaches were evaluated using Area Under the Curve (AUC) for Receiver Operating Characteristic (ROC), Precision recall and accuracy. Our data set comprised of a cohort of 1000 patients. The data was split 8:1 for training & testing respectively. The AUC ROC of the ML model was 0.86 with an accuracy of 75.7%. With LR AUC ROC was 0.74 with an accuracy of 73.8%. Feature importance chart showed that Glasgow coma score (GCS) at presentation had the highest relative importance, followed by hematoma volume and age in both approaches. Machine learning models perform better when compared to logistic regression. Models can be developed by clinicians possessing domain expertise and no programming experience using cloud based tools. The models so developed lend themselves to be incorporated into clinical workflow. © The Author(s) 2024.Item ProsGradNet: An effective and structured CNN approach for prostate cancer grading from histopathology images(Elsevier Ltd, 2025) Prabhu, A.; Sravya, N.; Lal, S.; Kini, J.Prostate cancer (PCa) is one of the most prevalent and potentially fatal malignancies affecting men globally. The incidence of prostate cancer is expected to double by 2040, posing significant health challenges. This anticipated increase underscores the urgent need for early and precise diagnosis to facilitate effective treatment and management. Histopathological analysis using Gleason grading system plays a pivotal role in clinical decision making by classifying cancer subtypes based on their cellular characteristics. This paper proposes a novel deep CNN model named as Prostate Grading Network (ProsGradNet), for the automatic grading of PCa from histopathological images. Central to the approach is the novel Context Guided Shared Channel Residual (CGSCR) block, that introduces structured methods for channel splitting and clustering, by varying group sizes. By grouping channels into 2, 4, and 8, it prioritizes deeper layer features, enhancing local semantic content and abstract feature representation. This methodological advancement significantly boosts classification accuracy, achieving an impressive 92.88% on Prostate Gleason dataset, outperforming other CNN models. To demonstrate the generalizability of ProsGradNet over different datasets, experiments are performed on Kasturba Medical College (KMC) Kidney dataset as well. The results further confirm the superiority of the proposed ProsGradNet model, with a classification accuracy of 92.68% on the KMC Kidney dataset. This demonstrates the model's potential to be applied effectively across various histopathological datasets, making it a valuable tool to fight against cancer. © 2025 Elsevier Ltd