H. K. Sardana

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Cephalometric analysis has an important role in diagnosis and treatment planning for malocclusions. Most analysis steps are computable and underlying structure may be generated provided landmarks are correctly localized. Due to the complexity of human anatomy sensed in a cephalometric x-ray, the landmarks are localized by human experts. In the last few(More)
A hybrid moving target detection approach in multi-resolution framework for thermal infrared imagery is presented. Background subtraction and optical flow methods are widely used to detect moving targets. However, each method has some pros and cons which limits the performance. Conventional background subtraction is affected by dynamic noise and partial(More)
Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a(More)
This paper involves the analysis and experimentation of chest CT scan data for the detection and diagnosis of lung cancer. In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. The contribution of this work include the development of lung nodule database with proven pathology using content(More)
Content Based Image Retrieval systems open new research areas in Computer Vision due to the high demand of image searching methods. CBIR is the process of finding relevant image from large collection of images using visual queries. The proposed system uses multiple image queries for finding desired images from database. The different queries are connected(More)
An efficient target detection algorithm for detecting moving targets in infrared imagery using spatiotemporal information is presented. The output of the spatial processing serves as input to the temporal stage in a layered manner. The spatial information is obtained using joint space-spatial-frequency distribution and Rényi entropy. Temporal information is(More)
In this paper, we present a novel approach to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of grafted decision tree based classifier in differentiating benign from malignant as well as from metastasis SPNs. We compared the results of smallest as well as largest nodule(More)
This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both(More)
In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. In this study, we have explored Lung Image Database Consortium (LIDC) database containing pulmonary computed tomography (CT) scans, and we developed contentbased image retrieval (CBIR) approach to exploit the limited amount of(More)