Harish Kumar Sardana

Automatic Craniofacial Structure Detection on Cephalometric Images

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 novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

Moving target detection in thermal infrared imagery using spatiotemporal information

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 incorporated using background subtraction. By utilizing both spatial and temporal information, it is observed that the proposed method can achieve both high detection and a low false-alarm rate. The method is validated with experimentally generated data consisting of a variety of moving targets. Experimental results demonstrate a high value of F-measure for the proposed algorithm.

Graphical Speech Training system for hearing impaired

Children who are born with handicapping hearing loss have limited acoustic speech target with which to imitate and compare their own speech and have to rely on visual cues of phonetic features to learn speech, articulation and to establish orosensory motor control of their speech movements. Computer aided Speech Training plays a significant role in developing speech and language skills of hearing impaired. A user-interactive graphical speech training system for hearing impaired has been discussed in this paper. A GUI has been developed for users of different age groups to learn new words and practice them with the help of visual feedback. The deaf childs performance is evaluated by the administrator, and visual feedback to be given is chosen accordingly. On integrating this with a robust speech recognition algorithm and by providing animated or game like features in the GUI, the software can motivate the hearing impaired to undergo effective training for longer hours and can polish their communication skills.

Semantic and content-based medical image retrieval for lung cancer diagnosis with the inclusion of expert knowledge and proven pathology

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 based image retrieval (CBIR) and algorithms for detection and classification of nodules. A study and analysis of 246 patients have been carried out for the detection of benign, malignant as well as metastasis nodules. The whole research work has been carried out using Lung Image Database Consortium (LIDC) database by National Cancer Institute (NCI), USA and achieved an average precision of 92.8% and mean average precision of 82% at recall 0.1. Finally, the validations have been carried out with the PGIMER, Chandigarh test cases and achieved an average precision of 88%. Experimental studies show that the proposed parameters and analysis improves the semantic performance while reducing the computational complexity, reading and analysing all slices by physicians and retrieval time.

Thermal Imaging And Its Application In Defence Systems

Thermal imaging is a boon to the armed forces namely army, navy and airforce because of its day night working capability and ability to perform well in all weather conditions. Thermal detectors capture the infrared radiation emitted by all objects above absolute zero temperature. The temperature variations of the captured scene are represented as a thermogram. With the advent of infrared detector technology, the bulky cooled thermal detectors having moving parts and demanding cryogenic temperatures have transformed into small and less expensive uncooled microbolometers having no moving parts, thereby making systems more rugged requiring less maintenance. Thermal imaging due to its various advantages has a large number of applications in military and defence. It is popularly used by the army and navy for border surveillance and law enforcement. It is also used in ship collision avoidance and guidance systems. In the aviation industry it has greatly mitigated the risks of flying in low light and night condi...

Precision of manual landmark identification between as-received and oriented volume-rendered cone-beam computed tomography images

Introduction The objective of this study was to evaluate the effect of the orientation of cone‐beam computed tomography (CBCT) images on the precision and reliability of 3‐dimensional cephalometric landmark identification. Methods Ten CBCT scans were used for manual landmark identification. Volume‐rendered images were oriented by aligning the Frankfort horizontal and transorbital planes horizontally, and the midsagittal plane vertically. A total of 20 CBCT images (10 as‐received and 10 oriented) were anonymized, and 3 random sets were generated for manual landmark plotting by 3 expert orthodontists. Twenty‐five landmarks were identified for plotting on each anonymized image independently. Hence, a total of 60 images were marked by the orthodontists. After landmark plotting, the randomized samples were decoded and regrouped into as‐received and oriented data sets for analysis and comparison. Means and standard deviations of the x‐, y‐, and z‐axis coordinates were calculated for each landmark to measure the central tendency. Intraclass correlation coefficients were calculated to analyze the interobserver reliability of landmark plotting in the 3 axes in both situations. Paired t tests were applied on the mean Euclidean distance computed separately for each landmark to evaluate the effect of 3‐dimensional image orientation. Results Interobserver reliability (intraclass correlation coefficient, >0.9) was excellent for all 25 landmarks for the x‐, y‐, and z‐axes on both before and after orientation of the images. Paired t test results showed insignificant differences for the orientation of volume‐rendered images for all landmarks except 3: R1 left (P = 0.0138), sella (P = 0.0490), and frontozygomatic left (P = 0.0493). Also midline structures such as Bolton and nasion were plotted more consistently or precisely than bilateral structures. Conclusions Orientation of the CBCT image does not enhance the precision of landmark plotting if each landmark is defined properly on multiplanar reconstruction slices and rendered images, and the clinician has sufficient training. The consistency of landmark identification is influenced by their anatomic locations on the midline, bilateral, and curved structures. HighlightsThis study provides insight into influence of orientation of CBCT image on 3D landmark plotting.Landmark plotting was performed by 3 observers blindly and randomly to prevent bias.Excellent interobserver reliability was obtained for as‐received and oriented CBCT data sets on landmark plotting.The locations of landmarks influence the consistency of their identification in both situations.

Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases

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 patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval (CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis.

Content Based Image Retrieval Approach in Creating an Effective Feature Index for Lung Nodule Detection with the Inclusion of Expert Knowledge and Proven Pathology

The paper investigates four major issues in the active field of lung computer aided diagnosis (CAD) using content-based image retrieval (CBIR), which are: creating an efficient feature index for lung nodules for similarity measures, database creation of nodules with proven pathology, robust CBIR system and present a self-diagnosing environment to assist the physician in taking the right decision at right time. The results definitely improves the radiologists performance of detecting suspicious nodules based on the ground truth prepared. CBIR has been implemented to expand the small ground truth of 17 nodules to ground truth of 114 nodules based on available biopsy report. Nine out of 83 different extracted features have been considered as the best discriminating features to classify the lung nodules in three classes: Malignant, Benign and Metastasis. LIDC database has been analysed and achieved an average precision of 92.8% , mean average precision (MAP) of 82% at recall 0.1 and an average precision of 88% with PGIMER, Chandigarh. Results in this paper also indicate that the unnecessary biopsies can be avoided as the results are having few number of false positives which can directly increase the specificity of the proposed research.

Computational techniques for classification of military vehicles using seismic signatures

In this research work a seismic classification system is designed to distinguish between tracked and wheeled vehicle classes. Owing to the extreme non-stationary nature of seismic signals, choosing robust features is an important aspect for the purpose of classification. To obtain a varied feature set different signal processing techniques namely Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), Hilbert-Huang Transform (HHT) and Wavelet Transform (WT) are investigated. Dominant features are identified from the feature bank using Principal Component Analysis (PCA). This choice of optimal and robust features leads to a better class discrimination. It is observed that the classification results obtained by the varied feature set followed by optimization has improved classification accuracy of 95% than using features extracted from individual signal processing techniques.

A novel strategy for automatic localization of cephalometric landmarks

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 years, efforts have been made to automate this process. This work contributes into a novel approach by first making a generic template search to make a coarse localization of the landmarks. A finer search further combines the best suitable edge or region based final localization. In both the steps of searching, small search area is allocated for each landmark. Films were digitized at three hundred dpi(dots per inch) and the experiments were conducted by recording fourteen landmarks on a number of patients by three dentists. Ground truth of the coordinates was obtained by averaging and the results of the algorithm were compared with these reference values. The result indicates that most of the landmarks lie in the specified limits. In this manner, both speed and accuracy is achieved with performance comparable to humans.