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Articles by H. Khanna Nehemiah
Total Records ( 2 ) for H. Khanna Nehemiah
  H. Khanna Nehemiah , A. Kannan and D. Senthil Kumar
  The retrieval of stored medical images matching an input medical image is an imperative form of content-based retrieval. For efficient similarity image retrieval and integration, the medical images should be processed systematically to extract a representing feature space vector for each member image. This study explains a system, which takes a fractured image as a query image and retrieves the similar images from the image database using distance metrics and also provides the radiologists with details about the type of fracture and the treatment recommended. The key objective of present research is to retrieve similar X-ray images of fractured reports using K-Nearest Neighbor. Images are matched using color in gray level and texture attributes. Similarity between images is established based on the respective numeric values (Signature). Features are extracted from X-ray images. Indexing is also performed on extracted features using a k-d tree data structure for images and is stored in a backend database for effective retrieval.
  H. Khanna Nehemiah and A. Kannan
  In this study we propose an Intelligent Lung Cancer Diagnosis System (ILCDS) that has been developed to detect all possible lung nodules from chest radiographs. Our system uses image processing techniques and feed forward neural networks for detection and validation of nodules. Nodules are relatively low-contrast white circular objects within the lung fields. As nodules are the most common sign of lung cancer, nodule detection in chest radiographs is a major diagnostic problem. Even experienced radiologists have trouble while distinguishing the normal pattern of blood vessels and nodules that indicate the Lung cancer. Our work is centered around two major sub systems namely Nodule Detection Subsystem (NDS) and Nodule Validation Subsystem (NVS). The Nodule Detection Subsystem is constructed using wavelet based image-processing techniques such as Besov ball projections, Laplacian of Gaussian filter and Gabor wavelet networks which are used to remove the noise from the image, find the edges of the image and detect the nodule, size and its location. The NDS detects all the possible nodules and gives the nodule-detected image. The processed image shows all nodules in the chest radiograph. Since all nodules are not cancerous, the nodules detected by the NDS are validated by the NVS. The NVS is constructed using Feed forward neural network classifiers, which classifies the nodules into non-cancerous and cancerous nodules.
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