Faculty Publications
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Item Study of malignancy associated changes in sputum images as an indicator of lung cancer(Institute of Electrical and Electronics Engineers Inc., 2017) Sudheesh, R.K.; Rajan, J.; Veena, V.S.; Sujathan, K.Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC) are the subtle changes that happens to the normal appearing cells near or distant from the malignant cells. Literature suggests that these changes can be used as an indicator for lung cancer rather than using malignant cells which are very less in number compared to the normal appearing cells in sputum cytology images. The proposed work is intended to detect cells with MAC from sputum smear images. Analysis of nuclei texture features of sputum cell nuclei using Gray Level Co-occurrence Matrix and Gray Level Run Length Matrix from both normal and cancer patients revealed that both type of cells could be differentiated. Among 110 texture features calculated for each nuclei, a set of 35 features which clearly distinguishes normal cells and normal appearing cells were chosen. Support Vector Machine (SVM) classifier is used to classify the cells into two classes i.e cells with MAC and cells without MAC. This study demonstrates that the presence of MAC cells in conventional microscopic sputum cytology images can be identified using image processing techniques and it can have some significance in the early detection of lung cancer. © 2016 IEEE.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 Recent Advancements in Retinal Vessel Segmentation(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Srinidhi, C.L.; Aparna., P.; Rajan, J.Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system. © 2017, Springer Science+Business Media New York.Item Guided SAR image despeckling with probabilistic non local weights(Elsevier Ltd, 2017) Gokul, J.; Nair, M.S.; Rajan, J.SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. © 2017 Elsevier LtdItem Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.Item Crossover based technique for data augmentation(Elsevier Ireland Ltd, 2022) Raj, R.; Mathew, J.; Kannath, S.K.; Rajan, J.Background and Objective: Medical image classification problems are frequently constrained by the availability of datasets. “Data augmentation” has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results. Methods: This paper introduces “Crossover technique”, a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures. Results: The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture. Conclusion: The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation © 2022Item 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 Ltd