International Journal of Recent Technology and Engineering (IJRTE), 2019
Lung cancer is one among the deadliest and dangerous widespread diseases that create a major publ... more Lung cancer is one among the deadliest and dangerous widespread diseases that create a major public health problem. The main aim of this paper is to basically segment the image or to identify the nodule present in the image and provide the accuracy of that segmented image. In this concern, proper segmentation of lung tumor present in the X-ray scans or Magnetic Resonance Imaging (MRI) or Computed Tomography (CT scan) is the first stone towards achieving completely automated diagnosis system for lung cancer detection of the patient. With the advanced technology and availability of dataset, the time required for a radiologist can be saved using CAD tools for tumor segmentation. In this work, we use an approach called data driven for lung tumor segmentation from CT scans by using UNet . In our approach we will train the network by using CT image with tumor having the slices of size (512 × 512 × 1). Our model has been trained and tested on the LUNA16 dataset considering 10 patients, pro...
Advances in Intelligent Systems and Computing, 2016
In this paper, a hybridization technique for multi-dimensional image segmentation algorithm is pr... more In this paper, a hybridization technique for multi-dimensional image segmentation algorithm is proposed. This algorithm is the combination of both the region growing and texture-based morphological algorithm of watersheds. An edge-preserving statistical noise reduction method is utilized as a preprocessing phase to calculate a perfect estimate of the image gradient. After that, a preliminary segmentation of the images into primitive regions is generated by employing the region growing. Then, watershed is applied. There are some drawbacks in medical mage study, when watershed is employed after the region growing. The main drawbacks are: over segmentation, sensitivity to noise and incorrect identification of thin or low signal to noise ratio structures. The main issue of over segmentation is controlled by texture local binary pattern (LBP). In addition, this paper has presented experimental outcomes achieved with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images. Numerical justification of the experimental outcomes is presented and demonstrated the efficiency of the algorithm for segmenting the medical image.
2020 IEEE International Conference for Innovation in Technology (INOCON)
Image stitching is a process of merging two or more images of the scene into single image of high... more Image stitching is a process of merging two or more images of the scene into single image of high resolution which is also termed as panoramic image. Image stitching is used to assimilate information from several images by overlapping view fields to create a panoramic view without loss of any information. Stitching of images can be performed by two types of common approaches such as direct and indirect techniques. Direct techniques involve direct comparison of image pixel intensities that are combining. Indirect techniques are dependent on image features. These techniques incorporate feature detection and feature matching of the images to be stitched. In this proposed work, efficient feature detection and feature matching techniques such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST), Euclidean distance and Random sample consensus (RANSAC) are used for the process of image stitching. Images of a scene are captured with fifteen degree difference. These images are pre-processed if needed and then fed to feature detection and matching process. Using the matched feature points, images are stitched to get a 360 degree scene in a single image as outcome. The FAST technique gives good matching points of images using RANSAC which help in obtaining a better stitched image with integrating all the data of different images involved in stitching. The experiment is carried out for two datasets and average detected feature points and matched points for three algorithms are computed. The SIFT algorithm gives 36 matched points among 319 detected points, SURF gives 55 matched points among 334 detected feature points and FAST gives 102 matched points among 774 detected feature points. Image stitching finds application in various fields such as medical imaging, satellite imagery, automobile industries, image stabilization, document mosaicing and further image stitching can be extended to video stitching.
The image segmentation is performed to detect, extract and characterize the anatomical structure.... more The image segmentation is performed to detect, extract and characterize the anatomical structure. Here, we apply two widely used algorithm for tumour detection (i) K-means clustering (ii) Fuzzy C Means clustering The segmentation algorithms are compared to estimate the efficiency by evaluating the execution time and accuracy of the algorithm. The result shows that the execution time is less in K-Means compared to Fuzzy C Means clustering technique, because the number of iterations of K-Means is less than Fuzzy C Means clustering. Further, the tumour area is calculated for accurate result
The need of security in present world is required to its maximum due to increasing terror attack.... more The need of security in present world is required to its maximum due to increasing terror attack. Secure face recognition is one such process that provides the best secured environment to reduce the malicious attack. It mainly deals with providing the best possible secured environment by fragmenting the face of human at client side and reconstructing the same human face at server side, thereby it avoids a chance of reducing malicious attack on security system and also helps to find the culprits easily. Inspite of many security measures are being taken by security centres and government, the disastrous of terror attack is unstoppable. Secure Identification of Face Recognition using Face Reconstruction, stands as an example of reducing such terror attack and make it more safer environment for passengers while travelling and also to find the culprits easily.
International Journal of Recent Technology and Engineering (IJRTE), 2019
Lung cancer is one among the deadliest and dangerous widespread diseases that create a major publ... more Lung cancer is one among the deadliest and dangerous widespread diseases that create a major public health problem. The main aim of this paper is to basically segment the image or to identify the nodule present in the image and provide the accuracy of that segmented image. In this concern, proper segmentation of lung tumor present in the X-ray scans or Magnetic Resonance Imaging (MRI) or Computed Tomography (CT scan) is the first stone towards achieving completely automated diagnosis system for lung cancer detection of the patient. With the advanced technology and availability of dataset, the time required for a radiologist can be saved using CAD tools for tumor segmentation. In this work, we use an approach called data driven for lung tumor segmentation from CT scans by using UNet . In our approach we will train the network by using CT image with tumor having the slices of size (512 × 512 × 1). Our model has been trained and tested on the LUNA16 dataset considering 10 patients, pro...
Advances in Intelligent Systems and Computing, 2016
In this paper, a hybridization technique for multi-dimensional image segmentation algorithm is pr... more In this paper, a hybridization technique for multi-dimensional image segmentation algorithm is proposed. This algorithm is the combination of both the region growing and texture-based morphological algorithm of watersheds. An edge-preserving statistical noise reduction method is utilized as a preprocessing phase to calculate a perfect estimate of the image gradient. After that, a preliminary segmentation of the images into primitive regions is generated by employing the region growing. Then, watershed is applied. There are some drawbacks in medical mage study, when watershed is employed after the region growing. The main drawbacks are: over segmentation, sensitivity to noise and incorrect identification of thin or low signal to noise ratio structures. The main issue of over segmentation is controlled by texture local binary pattern (LBP). In addition, this paper has presented experimental outcomes achieved with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images. Numerical justification of the experimental outcomes is presented and demonstrated the efficiency of the algorithm for segmenting the medical image.
2020 IEEE International Conference for Innovation in Technology (INOCON)
Image stitching is a process of merging two or more images of the scene into single image of high... more Image stitching is a process of merging two or more images of the scene into single image of high resolution which is also termed as panoramic image. Image stitching is used to assimilate information from several images by overlapping view fields to create a panoramic view without loss of any information. Stitching of images can be performed by two types of common approaches such as direct and indirect techniques. Direct techniques involve direct comparison of image pixel intensities that are combining. Indirect techniques are dependent on image features. These techniques incorporate feature detection and feature matching of the images to be stitched. In this proposed work, efficient feature detection and feature matching techniques such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST), Euclidean distance and Random sample consensus (RANSAC) are used for the process of image stitching. Images of a scene are captured with fifteen degree difference. These images are pre-processed if needed and then fed to feature detection and matching process. Using the matched feature points, images are stitched to get a 360 degree scene in a single image as outcome. The FAST technique gives good matching points of images using RANSAC which help in obtaining a better stitched image with integrating all the data of different images involved in stitching. The experiment is carried out for two datasets and average detected feature points and matched points for three algorithms are computed. The SIFT algorithm gives 36 matched points among 319 detected points, SURF gives 55 matched points among 334 detected feature points and FAST gives 102 matched points among 774 detected feature points. Image stitching finds application in various fields such as medical imaging, satellite imagery, automobile industries, image stabilization, document mosaicing and further image stitching can be extended to video stitching.
The image segmentation is performed to detect, extract and characterize the anatomical structure.... more The image segmentation is performed to detect, extract and characterize the anatomical structure. Here, we apply two widely used algorithm for tumour detection (i) K-means clustering (ii) Fuzzy C Means clustering The segmentation algorithms are compared to estimate the efficiency by evaluating the execution time and accuracy of the algorithm. The result shows that the execution time is less in K-Means compared to Fuzzy C Means clustering technique, because the number of iterations of K-Means is less than Fuzzy C Means clustering. Further, the tumour area is calculated for accurate result
The need of security in present world is required to its maximum due to increasing terror attack.... more The need of security in present world is required to its maximum due to increasing terror attack. Secure face recognition is one such process that provides the best secured environment to reduce the malicious attack. It mainly deals with providing the best possible secured environment by fragmenting the face of human at client side and reconstructing the same human face at server side, thereby it avoids a chance of reducing malicious attack on security system and also helps to find the culprits easily. Inspite of many security measures are being taken by security centres and government, the disastrous of terror attack is unstoppable. Secure Identification of Face Recognition using Face Reconstruction, stands as an example of reducing such terror attack and make it more safer environment for passengers while travelling and also to find the culprits easily.
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Papers by Rajaram Gowda