Deepti Mittal

Automated detection and segmentation of drusen in retinal fundus images

Designed a new drusen detection and segmentation method finding meaningful drusen boundaries.To find true edges of drusen, a gradient based segmentation procedure is described.Connected component labeling is applied to remove suspicious pixels from drusen region.Edge linking is used to connect all labeled pixels into a meaningful boundary to detect drusen.The performance of proposed method is evaluated by (i) statistical measures and (ii) quantification of drusen to grade severity of age-related macular degradation.The proposed work characterizes the detected drusen in small, intermediate, and large/soft to show its ability to grade age-related macular degradation severity level, helpful in early age-related macular degradation diagnosis. The druse, an abnormal yellow/white deposit on retina, is a dominant characteristic of age-related macular degeneration (AMD) which is a retinal disorder associated with age. The early detection of drusen is useful for ophthalmologists to diagnose the patients that suffer from AMD. An automated method has been proposed in this work to detect and segment drusen using retinal fundus images by (i) gradient based segmentation to find true edges of drusen, (ii) connected component labeling to remove suspicious pixels from drusen region and (iii) edge linking to connect all labeled pixels into a meaningful boundary. The proposed method outperforms other existing methods in detection of drusen with an accuracy/sensitivity/specificity of 96.17/89.81/99.00 on two publicly available retinal image databases. In order to grade the severity of AMD, the detected drusen by the proposed method are further quantified into small, intermediate and large with an accuracy of 88.46, 98.55, and 88.37%, respectively. Display Omitted

Secure Data Mining in Cloud Using Homomorphic Encryption

With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.

Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging A Review

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable “second opinion” for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

Impact of Modified Anisotropic Diffusion–Based Enhancement Method in Computer-Aided Classification of Focal Liver Lesions

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion–based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves. The results reveal that the application of enhancement method on clinically acquired ultrasound image may effectively improve the confidence of clinicians/radiologists in computer-aided diagnostic solutions to detect and classify focal liver lesions.

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of “handcrafted” texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k–nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of “handcrafted” texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix.

Comparative study of fetal ECG elicitation using adaptive filtering techniques

Fetal electrocardiogram (f-ECG) elicitation is crucial for cardiac health monitoring of fetus and neonates. This work presents five non-invasive f-ECG elicitation techniques using Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Sign-error Least Mean Square (SELMS), Affine Projection Algorithm (AP), and Recursive Least Square (RLS). The performance of the algorithms is compared on the basis of correlation index between original and recovered signal. Computational complexity per iteration of the algorithms is analyzed on the basis of a parameter, computational cost. RLS out-performed all other algorithms in terms of performance and SELMS is better than LMS and its variants, both in terms of performance and computational complexity.

Comparative evaluation of two segmentation algorithms: Application on liver segmentation of CT abdomen images

Liver segmentation plays a crucial role mainly in tumor segmentation, 3D volume reconstruction and treatment planning of liver disease. In the present work, a comparative evaluation of two segmentation methods named as active contour and level set is presented. Comparative evaluation of both methods has been done individually by applying on Computed Tomography (CT) abdomen images. Comparative study is done by following steps (i) Preprocessing using morphological functions (ii) Segmentation of liver using active contour/level set algorithms (iii) 3-Dimensional volume reconstruction. Results of applied liver segmentation algorithms are compared with manually performed delineations by radiologist. Volume overlapping of above methods is assessed using Dice similarity coefficient (DSC) which gives mean DSC of 93.52%±.62 and 89.48%±.76 in case of active contour and level set method respectively. Finally it can be demonstrated that active contour is superior to level set in terms of accuracy, processing time and 3D volume reconstruction.

A Novel Hybrid Method for Segmentation of Ultrasound Images

Ultrasound (US) is a very convenient and safe diagnostic tool to distinguish benign from malignant masses of the body. As subjective interpretation is time consuming so a computer-aided segmentation approach is needed to assist doctors to estimate tumor margin, and it helps in providing real-time targeted image guidance during surgery. But due to higher noise in Ultrasound image segmentation is a challenging task. In this paper a novel and robust algorithm has been proposed for edge detection of diseased area for effective clinical use. The algorithm divides itself in four stages. Initially, thresholding using moving averages is done to overcome nonuniform illumination of US image which is followed by edge detection with different gradient masks. Morphological operations are used to carve out ROI. Results with several US images with various levels of noises are used to demonstrate the effectiveness of the proposed approach.

Performance of CT metastases and compare their results with others

Classification of normal liver and different types of tumors in the liver by using Computed Tomography (CT) imaging technique. The processing of image enhancement is done by using Contrast limited adaptive histogram equalization (CLAHE) algorithm. That enhanced images have a different view look for normal liver and both tumors. A number of parameters evaluations for the comparison between both types of tumors and to get the level of tumors. A desired better range of parameters work has a small spam in the values of the range. Large amount of input image data in one particular record work for better response.