Browsing by Author "Sengan, Sudhakar"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM(Tech Science Press, 2022) Priyadarsini S.; Tavera Romero, Carlos Andres; Mehbodniya, Abolfazl; Sagar, P. Vidya; Sengan, SudhakarIn the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-Angle Texture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from its neighbouring soft tissues with related strength. Then, the filtered images are given as the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection (CED) technique, which segments the Liver Region (LR) from the given CT images. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error (VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applying the MAT-ACM, which accurately segments the LR from the segmented LRs. The evaluation of the proposed method is compared with the existing LTS methods using various performance measures to prove the superiority of the proposed MAT-ACM method.Item Classification of Liver Tumors from Computed Tomography Using NRSVM(Tech Science Press, 2022) Priyadarsini S.; Tavera Romero, Carlos Andrés; Mrunalini M.; Koteswara Rao, Ganga Rama; Sengan, SudhakarA classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region (LR) from the surrounding tissues, Non-Linear Mapping (NLP) is used. Region Growing Segmentation (RGS) is employed to segment the LR, and Expectation-Maximization (EM) algorithm is used to segment the region of interest. Grey Level Co-occurrence Matrix (GLCM) features are extracted from the tumor region, and Nonlinear Random Support Vector Machine (NRSVM) classification is performed to classify the Benign Tumors and Malignant Tumors. The proposed method is tested on a database of medical images collected from Med all Diagnostic Research Centre and attained an accuracy of 96%. The proposed method is beneficial for better liver tumor diagnosis in an optimized method by the medical expert.Item Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky(Institute of Electrical and Electronics Engineers Inc., 2022) Karn, Arodh Lal; Tavera Romero, Carlos Andres; Sengan, Sudhakar; Mehbodniya, Abolfazl; Webber, Julian L.; Pustokhin, Denis A.; Wende, Frank-DetlefThe Sloan Digital Sky Survey (SDSS) comprises about one billion objects classified spectrometrically. Because astronomical datasets are so enormous, manually classifying them is nearly impossible - a huge dataset results in class imbalance and overfitting. We recommend a framework in this research study that overcomes these constraints. The framework uses a hybrid Synthetic Minority Oversampling Technique + Edited Nearest Neighbor (SMOTE + ENN) balancer. The balanced dataset is then used to extract features via a non-linear algorithm using Kernel Principal Component Analysis (KPCA). The features are then passed into the proposed Int-T2-Fuzzy Support Vector Machine classifier, which uses a modified type reducer and inference engine to achieve more precise categorization. Using the Sloan Digital Sky Survey dataset and a number of evaluation metrics, the SMOTE+ENN model's performance is measured. The research shows that the model does a good job.Item Interactive middleware services for Heterogeneous systems(Tech Science Press, 2022) Raghupathy, Vasanthi; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos Andrés; Sengan, Sudhakar; Sharma, Dilip KumarComputing has become more invisible, widespread and ubiquitous since the inception of the Internet of Things (IoT) and Web of Things. Multiple devices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapid development of interactive services in Heterogeneous Systems. This resulted in the delivery of interactive services throughout Heterogeneous Environments (HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, Smart Phones, Mobile Phones, Personal Computer (PC) and Personal Digital Assistant (PDA)) are available in the market. For easy access to information and services irrespective of the device used for working and even at the drastic change of the environment, the execution of applications on a broad spectrum of computing devices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with each other should be facilitated. Using a standard interface and data format, HE must link various devices from various platforms together to communicate with each other. To aid the interactive services performed by a middleware framework that operates on Application Programming Interface (API) over HEs, this issue aims to endorse an Adaptable Service Application Programming Interface (ASAPI).