2. Thesis and Dissertations

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    Early Diagnosis of Osteoporosis using Metacarpal Radiogrammetry and Texture Analysis
    (National Institute of Technology Karnataka, Surathkal, 2019) Areeckal, Anu Shaju.; S, Sumam David
    Osteoporosis is a disease characterized by reduction in bone mass and micro-structure, leading to increased risk of fragility fractures. The gold standard technique used for diagnosis of osteoporosis is determination of Bone Mineral Density (BMD) using Dual Energy X-ray Absorptiometry (DXA). DXA is accurate and precise, however it has a high cost of scan and low availability in developing countries. The aim of the research work is to develop a low cost prescreening tool for early diagnosis of osteoporosis using cortical radiogrammetry and trabecular texture analysis of hand and wrist radiographs. An automatic method for segmentation of the third metacarpal bone shaft from hand and wrist radiographs is proposed using automatically detected anatomical landmarks, intensity profiles and marker-controlled watershed segmentation. From the outer and inner bone edges of the segmented third metacarpal bone shaft, cortical radiogrammetric features are extracted. The proposed method is validated on sample data of two ethnic groups: 138 Indian subjects and 65 Swiss subjects. The proposed segmentation method accurately detected the third metacarpal bone in 89% of Indian sample data and 78% of Swiss sample data. The proposed method shows better performance as compared with the state-of-the-art segmentation method, Active Appearance Model (AAM). A segmentation approach is proposed to automatically extract the distal radius for trabecular texture analysis. The proposed method extracted the distal radius region-ofinterest in 93.5% of Indian sample data and 83% of Swiss sample data. Texture analysis of the trabecular bone in distal radius is done. The extracted features are analyzed using independent sample t-test and Pearson correlation analysis. The cortical radiogrammetric features show high discrimination ability in the healthy and low bone mass groups of both Indian and Swiss sample data. The cortical and texture features are divided into different feature sets. Classifiers are trained on cortical features and statistical and structural texture features for Inviidian sample data and a linear regression model is estimated. Artificial Neural Network (ANN) classifier trained using holdout validation achieves test accuracy of 90.0%. kNearest Neighbor (KNN) using 10-fold cross validation achieves an accuracy of 81.7%. The linear regression model developed with the cortical and texture features achieves a significant correlation of 0.671 with DXA-BMD. Classifiers are also trained separately for Indian and Swiss sample population. ANN classifiers trained with significant cortical and statistical and structural texture features show test accuracy of 92.9% with Indian data and 90.9% with Swiss data. Weighted KNN using the same feature set shows test accuracy of 96.2% using holdout validation. A novel method to measure the cortical volume of the metacarpal bone shaft at a low cost using three dimensional reconstruction from hand X-ray images in three views (Postero-Anterior, 450 and 1350 oblique views) is proposed. The Computed Tomography scan of one subject is used to create a template model, from which subject-specific models of other subjects are reconstructed. The 3D reconstruction of the bone is done iteratively by registration of projection and X-ray contours using Iterative Closest Point and Self-Organizing Map, and deformation of the template model using Laplacian surface deformation. The outer and inner bone walls of the metacarpal are modeled separately and the third metacarpal bone shaft is extracted from which cortical volume is measured. The projections of the 3D reconstructed models are compared with manually segmented X-ray images and the mean percentage error in Combined Cortical Thickness (CCT) is 11.18%. In summary, a low cost prescreening tool for early diagnosis of osteoporosis using cortical radiogrammetry and texture analysis is proposed and validated using sample data of Indian and Swiss population. A low cost method to measure cortical volume of third metacarpal bone shaft using multi-view hand radiographs is also proposed. This work is done in collaboration with Kasturba Medical College Hospital, Mangalore, India and University Hospital of Geneva, Switzerland.
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    Development of CAD System for Detection, Classification, Retrieval and 3d Reconstruction of Brain and Liver Tumors on MRI and CT Images
    (National Institute of Technology Karnataka, Surathkal, 2014) Arakeri, Megha P.; Reddy, G. Ram Mohana
    Brain and liver tumors are the life-threatening diseases due to low survival rate. Hence, accurate diagnosis of brain and liver tumors is necessary to provide effective treatment. Medical imaging techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) help in acquiring images of the tumor. The visual analysis of these medical images by the radiologist is time consuming, subjective and inaccurate. The needle biopsy of the tumor provides accurate diagnosis but it is an invasive technique and generally not recommended. In order to overcome these drawbacks, there is a need to develop Computer-Aided Diagnosis (CAD) system for assisting the radiologist in fast and accurate diagnosis of tumors. Therefore, this thesis proposes an effective and efficient CAD system for tumor detection, classification, Content-Based Image Retrieval (CBIR), and 3D reconstruction to provide complete assistance to the radiologist in the diagnosis of brain and liver tumors. In the first methodology, this thesis aims at tumor detection, by proposing automatic, effective and efficient segmentation methods for brain and liver tumors on medical images. The brain tumor is detected using the proposed segmentation technique based on Modified Fuzzy C-Means (MFCM) clustering algorithm. The liver tumor is detected using the proposed segmentation technique based on the automatic region growing algorithm. In the second methodology, this thesis targets at identification of the type of brain/liver tumor as benign or malignant, by proposing an effective and efficient tumor classification scheme. Precisely, the proposed scheme represents the tumor characteristics using its significant features, selects most discriminating features using a two-level feature selection technique consisting of Information Gain (IG) based feature ranking and Independent Component Analysis (ICA) based feature section methods. Then, the tumor is classified using an ensemble classifier consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbor (k-NN) classifiers. In the third methodology, this thesis proposes two CBIR methods based on image rotation correction and rotation invariant features to assist the radiologist in brain/liver tumor diagnosis based on past resolved cases. In order to provide fast retrieval of tumor images from the database, the tumor features in the database are indexed using the proposed indexing technique called as Cluster with IG-ICA and KD-tree (CIKD). The features in the database are partitioned into different groupsusing modified k-means clustering which identifies the number of clusters and initial cluster centers automatically. In the fourth methodology, this thesis aims to build the 3D model of the brain/liver tumor, by proposing an effective and efficient 3D reconstruction scheme. Precisely, it proposes an enhanced shape-based interpolation algorithm to estimate missing slices in a given set of brain/liver tumor slices. Further, the 3D mesh simplification algorithm is proposed to reduce the number of triangles in the reconstructed mesh and accelerate the rendering phase. The tumor volume is also computed to assist the radiologist in estimating the stage of cancer. Experiments are carried out on a dataset consisting of MRI images of the brain tumor and CT images of the liver tumor. Experimental results demonstrate that the proposed CAD system is automatic, effective and efficient in the diagnosis of brain and liver tumors.
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    Effective Multimedia Document Representations for Knowledge Discovery
    (National Institute of Technology Karnataka, Surathkal, 2017) K, Pushpalatha; V. S, Ananthanarayana
    In recent years, the rapid advances in multimedia technology have led to grow the multimedia documents explosively. In order to utilize the multimodal information of multimedia documents, sophisticated knowledge discovery systems are required. The knowledge discovery systems require efficient multimedia mining methods to extract the meaningful and useful information from the huge volume of multimedia documents. The success of multimedia mining relies on the representation of multimedia documents and its multimodal contents. The appropriate representation of multimedia documents discovers the useful patterns that can be used to assist the multimedia mining methods in discovering the useful knowledge. The multimodal nature of multimedia objects is the challenging problem for the multimedia document representation, as the features of multimodal objects are in different space with different characteristics and dimensionalities. Representation of multimodal multimedia objects in a unified feature space helps the multimedia document representation and multimedia mining methods. The research work in this thesis proposes the multimedia data representation methods, multimedia document representations, and multimedia mining methods for the effective knowledge discovery in multimedia documents. In the first methodology, this thesis aims at the representation of multimodal multimedia objects in a unified feature space. We propose two multimedia data representation methods, Multimedia To Signal Conversion (MSC) and Multimedia to Image Conversion (MIC) to represent the multimedia objects in a unified domain. The MSC represents the multimedia objects in frequency domain by converting the multimedia objects as signal objects. The MIC converts the multimedia objects as image objects to represent them in spatial domain. The multimedia objects in unified domain are represented in the unified feature space using the features with similar dimensions and characteristics. Hence, both the multimedia data representation methods convert themultimodal multimedia documents as unified multimedia documents. The unified multimedia documents ease the representation of multimedia documents and improve the efficiency of multimedia mining methods. The proposed multimedia data representation methods are effectively used for knowledge extraction from multimedia documents. In the second methodology, this thesis presents the two multimedia document representations, Multimedia Suffix Tree Document (MSTD) and Multimedia Feature Pattern Tree (MFPT) to represent the unified multimedia documents. The MSTD represents the unified multimedia documents based on shared similar multimedia objects among the documents. The similarity between the multimedia objects depends on the similarity of the features. The MFPT represents the documents based on shared similar feature patterns of the multimedia objects. Both the representations are compact and provide the complete information of the documents. They function as the platform for the multimedia knowledge extraction methods. In the third methodology, this thesis explores the multimedia mining methods based on the MSTD and MFPT representations. The MSTD and MFPT based classification algorithms effectively classifies the multimedia documents. The multimedia documents are partitioned into clusters of same multimedia concepts using the MSTD and MFPT based clustering algorithms. The MSTD representation extracts the frequent multimedia patterns to generate the multimedia class association rules for classifying the multimedia documents. The MFPT representation extracts the sequential multimedia feature patterns to derive the multimedia class sequential rules that support the classification of multimedia documents based on the object characteristics. The efficacy of the proposed methods is evaluated by conducting the experiments with four datasets of multimodal multimedia documents. Experimental results demonstrate that the proposed multimedia data representation methods benefit the multimedia document representation and multimedia mining methods by representing the multimodal multimedia objectsin a unified feature space. The proposed multimedia document representations are effectively used to enhance the performance of multimedia mining methods in discovering the knowledge from multimedia documents.