Qualification: 
Ph.D, M.E, B-Tech
viminaer@asas.kh.amrita.edu

Dr. E. R. Vimina currently serves as Assistant Professor in the Department of Computer Science and I.T., School of Arts & Sciences, Amrita Vishwa Vidyapeetham, Kochi.

Qualification: Ph. D. (Computer Science), M. E. (Computer Science and Engineering), B. Tech. (Electrical and Electronics)

Publications

Publication Type: Journal Article

Year of Publication Title

2021

S. Sujamol, E. R. Vimina, and Krishnakumar, U., “Improving Recurrence Prediction Accuracy of Ovarian Cancer Using Multi-phase Feature Selection Methodology”, Applied Artificial Intelligence, vol. 35, pp. 206-226, 2021.[Abstract]


ABSTRACTOvarian cancer stands in the sixth position among the most commonly occurring cancers in the world. Because of the high rate of recurrence, this gynecological malignancy seems to be a vital reason behind cancer-related death among women as tumor recurrence stands as an obstacle in ovarian cancer treatment. It is crucial to find those recurrence causing factors in order to plan suitable therapies with high prognostic results. Hence, in this work, a multistage feature selection methodology is proposed to identify key MiRNAs and clinical features for improving the accuracy of ovarian cancer recurrence prediction. MiRNA expression profiles of ovarian cancer patients and their corresponding clinical data were downloaded from the TCGA cancer repository. From 588 MiRNAs, 6 key MiRNAs were selected using the Inheritable Bi-objective Combinatorial Genetic Algorithm (IBCGA) followed by factor analysis. The biological importance of the resultant MiRNAs in cancer and cellular pathways were studied using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Further, recurrence prediction was performed using the obtained MiRNA expression profiles and clinical factors, chosen using correlation analysis. The proposed approach using the selected features yielded a prediction accuracy of 91.86% using the XGBoost classifier while the same without feature selection was 76.59%. Compared to previous similar works, this model provides a better result in terms of accuracy and reveals influential MiRNAs in ovarian cancer.

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2021

R. Arya and E. R. Vimina, “Local Triangular Coded Pattern: A Texture Descriptor for Image Classification”, IETE Journal of Research, pp. 1-12, 2021.[Abstract]


Local binary descriptors are extensively used for image representation in many of the computer vision applications. A majority of these local binary descriptors exploit the intensity difference of the neighbouring pixels with respect to the centre pixel of the chosen region to formulate the representative value at the respective pixel position. In this paper, a novel descriptor, called Local Triangular Coded Pattern (LTCP), is introduced that utilises the relationship between a set of pixels in the triangular neighbourhood of a region to compute the descriptor. Unlike many of the other local binary descriptors, the proposed descriptor considers multiple pixels as centres within the given region to obtain the binary pattern. The performance of the LTCP descriptor is analysed by performing image classification in benchmarked texture datasets such as KTH-TIPS, Outex, Brodatz and Kylergb and in facial emotion datasets such as CK+, JAFFE, MUFE and Yale Face. The results indicate that LTCP with the Random Forest classifier gives an accuracy of 92.82%, 93.81%, 94.11% and 97.14%, respectively, on Brodatz, Outex, KTH-TIPS and Kylergb datasets for texture classification and 97.52%, 95.52%, 96.13% and 93.88%, respectively, on CK+, JAFFE, MUFE and Yale Face datasets for emotion classification. The experimental findings reflect the LTCP descriptor’s dominance and robustness over others.

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2021

U. Krishnakumar, Suresh, N. T., and E. R. Vimina, “A Computational Framework to Identify Cross Association Between Complex Disorders by Protein-protein Interaction Network Analysis”, Current Bioinformatics, vol. Volume 16, pp. Page: 433 – 445, 2021.[Abstract]


Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore the association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the constructed Protein-Protein Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive key genes and biological pathways shared among diseases.
Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen disease genes in order to infer its associations with other diseases in terms of shared pathways, coexpression, co-occurrence etc. For this, initially, proteins associated to any disease based on random choice were identified. Secondly, PPIN is organized through topological analysis to define hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways involved in multimorbidity-associated proteins are mined.
Result and Conclusion: The proposed methodology is tested using three disorders, namely Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of other complex diseases that have associations with the proteins included in the disease of present study through shared proteins and pathways. For diabetes, we have obtained key genes like GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin signaling pathway, Huntington disease etc. Studies on other disorders such as obesity and blood pressure also revealed promising results.

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2020

N. T. Suresh, E. R. Vimina, and U., K., “Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways”, Computational Biology and Chemistry, vol. 88, p. 107323, 2020.[Abstract]


Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.

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2020

M. O. Divya and E. R. Vimina, “Content based image retrieval with multi-channel LBP and colour features”, International Journal of Applied Pattern Recognition, vol. 6, no. 2, pp. pages: 177-193, 2020.[Abstract]


Content based image retrieval (CBIR) systems are used for retrieving relevant images from datasets in response to a query based on image features. The features used for describing the image content play crucial role in determining the efficacy of the CBIR. In this paper a texture-colour fusion method exploiting the multi-channel information of colour images is proposed for describing the image content. Texture is represented with multi-channel local binary adder pattern, computed by considering all the channels of a colour image, and colour information is computed by quantising the constituent colour channels of the image. The method exploits RGB colour space for feature extraction. Experimental results show respective average retrieval precisions of 80.73%, 60.096%, 48.22% and 69.89% in the Wang's, Corel 5k, Corel 10k and Zubud datasets using the proposed feature combination. Comparative analysis indicates that the proposed approach has an edge over many other recent methodologies under consideration.

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2020

Divya M. O. and E. R. Vimina, “Maximal multi-channel local binary pattern with colour information for CBIR”, Multimedia Tools and Applications, vol. 79, pp. 25357–25377, 2020.[Abstract]


Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel Local Binary Pattern (MMLBP), which integrates the multi-channel local binary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang’s 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.

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2019

Divya M. O. and E. R. Vimina, “Performance Analysis of Distance Metric for Content Based Image Retrieval”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6, 2019.[Abstract]


Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

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2019

E. R. Vimina and K. Poulose Jacob, “Feature Fusion Method using BoVW Framework for Enhancing Image Retrieval”, IET Image Processing, vol. 13, no. 11, pp. 1979 – 1985, 2019.[Abstract]


The bag-of-visual words (BoVW) has been applied to myriad of recognition problems in computer vision such as object recognition, scene classification and image retrieval due to its scalability and high precision. However, their performance is subservient in certain datasets, especially in natural image datasets, mainly due to the lack of consideration of image cues such as colour, texture etc. which are not prime features while computing invariant descriptors, on which BoVW models are generally built on. Hence, this study describes a multi-cue fusion approach for BoVW framework, exploiting both early and late fusion methods, to improve the retrieval performance, mainly in natural image datasets. For this, a composite edge and colour descriptor is proposed to describe the local regions of the image along with the invariant feature descriptor Speeded Up Robust Features (SURF). Independent vocabularies are built based on these descriptors and images in the dataset are encoded to form two histograms using the respective vocabularies. The histograms are further fused to characterize the image. The retrieval is carried out by matching the histograms. Experimental results show that significant increment in the average precision can be attained by combining the proposed descriptor with invariant descriptors.

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Publication Type: Conference Paper

Year of Publication Title

2020

L. Suresh, Chandran, S., Vijayan, D., and E. R. Vimina, “An Efficient method to Retrieve Diabetic Retinopathy Images using CBIR Technique”, in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020.[Abstract]


In this paper, an efficient method for Content Based Image Retrieval (CBIR) to retrieve the images of diabetic retinopathy (DR) is proposed. The methodology involves inter-plane relationship between pixel intensities and feature reduction. Key pixels are selected from an edgy image and are used for computing the intensities in inter-plane relationship. By using the selected point as a center pixel the Local Binary Patterns (LBPs) are computed. Our approach enhanced the results by compressing the size of the resultant metrics. Feature reduction is performed by using the Random Bin Selection method. The experiments conducted on the STARE dataset indicates an increment of 42.04% in the average precision rate compared to the existing method.

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Publication Type: Book Chapter

Year of Publication Title

2020

A. R. and E. R. Vimina, “An Evaluation of Local Binary Descriptors for Facial Emotion Classification”, in Innovations in Computer Science and Engineering: Proceedings of 7th ICICSE, S. Harvinder Singh, Sayal, R., Buyya, R., and Aliseri, G., Eds. Singapore: Springer Singapore, 2020, pp. 195–205.[Abstract]


Feature descriptors are vitally important in the broad domain of computer vision. In software systems for face recognition, local binary descriptors find wide use as feature descriptors. Because they give more robust results in varying conditions such as pose, lighting and illumination changes. Precision depends on the correctness of representing the relationship in the local neighbourhood of a digital image into small structures. This paper presents the performance analysis of various binary descriptors such as local binary pattern (LBP), local directional pattern (LDP), local directional number pattern (LDNP), angular local directional pattern (ALDP), local optimal-oriented pattern (LOOP), support vector machine (SVM), K-nearest neighbour (KNN) and back propagation neural network (BPNN) are used for emotion classification. The results indicate that ALDP\thinspace+\thinspacePolynomial SVM on MUFE, JAFFE and Yale Face databases gives better accuracy with 96.00%, 94.44% and 89.00%, respectively.

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