Sudeb Das

BRAIN MR IMAGE CLASSIFICATION USING MULTISCALE GEOMETRIC ANALYSIS OF RIPPLET

We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.

Effective management of medical information through ROI-lossless fragile image watermarking technique

In this article, we have proposed a blind, fragile and Region of Interest (ROI) lossless medical image watermarking (MIW) technique, providing an all-in-one solution tool to various medical data distribution and management issues like security, content authentication, safe archiving, controlled access retrieval, and captioning. The proposed scheme combines lossless data compression and encryption technique to embed electronic health record (EHR)/DICOM metadata, image hash, indexing keyword, doctor identification code and tamper localization information in the medical images. Extensive experiments (both subjective and objective) were carried out to evaluate performance of the proposed MIW technique. The findings offer suggestive evidence that the proposed MIW scheme is an effective all-in-one solution tool to various issues of medical information management domain. Moreover, given its relative simplicity, the proposed scheme can be applied to the medical images to serve in many medical applications concerned with privacy protection, safety, and management.

Medical Image Fusion Based on Ripplet Transform Type-I

The motivation behind fusing multimodality, multi- resolution images is to create a single image with improved interpretability. In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) for spatially registered, multi-sensor, multi-resolution medical images. RT is a new Multi-scale Geometric Analysis (MGA) tool, capable of resolving two dimensional (2D) singularities and representing image edges more e-ciently. The source medical images are flrst transformed by discrete RT (DRT). Difierent fusion rules are applied to the difierent subbands of the transformed images. Then inverse DRT (IDRT) is applied to the fused coe-cients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis shows, that the proposed technique performs better compared to fusion scheme based on Contourlet Transform (CNT).

Lossless ROI Medical Image Watermarking Technique with Enhanced Security and High Payload Embedding

In this article, a new fragile, blind, high payload capacity, ROI (Region of Interest) preserving Medical image watermarking (MIW) technique in the spatial domain for gray scale medical images is proposed. We present a watermarking scheme that combines lossless data compression and encryption technique in application to medical images. The effectiveness of the proposed scheme, proven through experiments on various medical images through various image quality measure matrices such as PSNR, MSE and MSSIM enables us to argue that, the method will help to maintain Electronic Patient Report(EPR)/DICOM data privacy and medical image integrity.

Effective Management of Medical Information Through A Novel Blind Watermarking Technique

Medical Data Management (MDM) domain consists of various issues of medical information like authentication, security, privacy, retrieval and storage etc. Medical Image Watermarking (MIW) techniques have recently emerged as a leading technology to solve the problems associated with MDM. This paper proposes a blind, Contourlet Transform (CNT) based MIW scheme, robust to high JPEG and JPEG2000 compression and simultaneously capable of addressing a range of MDM issues like medical information security, content authentication, safe archiving and controlled access retrieval etc. It also provides a way for effective data communication along with automated medical personnel teaching. The original medical image is first decomposed by CNT. The Low pass subband is used to embed the watermark in such a way that enables the proposed method to extract the embedded watermark in a blind manner. Inverse CNT is then applied to get the watermarked image. Extensive experiments were carried out and the performance of the proposed scheme is evaluated through both subjective and quantitative measures. The experimental results and comparisons, confirm the effectiveness and efficiency of the proposed technique in the MDM paradigm.

Hybrid contourlet-DCT based robust image watermarking technique applied to medical data management

This paper describes a non-blind, imperceptible and highly robust hybridMedical ImageWatermarking (MIW) technique for a range of medical data management issues. The method simultaneously addresses medical information security, content authentication, safe archiving and controlled access retrieval. We propose the use of Contourlet Transform (CLT) followed by the Discrete Cosine Transform (DCT) to achieve higher robustness and imperceptibility. Experimental results and performance comparisons confirm the effectiveness and efficiency of the proposed scheme.

Interactive content based image retrieval using ripplet transform and fuzzy relevance feedback

In this article, a novel content based image retrieval (CBIR) system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result, a fuzzy relevance feedback mechanism (F-RFM) is also implemented. Fuzzy entropy based feature evaluation mechanism is used for automatic computation of revised features importance and similarity distance at the end of each iteration. Experimental results on a large image database demonstrate the efficiency and effectiveness of the proposed CBIR system in the image retrieval paradigm

Ripplet based multimodality Medical Image Fusion using Pulse-Coupled Neural Network and Modified Spatial Frequency

In this paper, a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) using Pulse-Coupled Neural Network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both RT and PCNN to obtain better results. The source medical images are first decomposed by discrete RT (DRT). The low-frequency subbands (LFSs) are fused using the ‘max selection’ rule. For the fusion of high-frequency subbands (HFSs) a PCNN model is utilized. Modified Spatial Frequency (MSF) in DRT domain is input to motivate the PCNN and coefficients in DRT domain with large firing times are selected as coefficients of the fused image. Then inverse DRT (IDRT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis and comparisons show the effectiveness of the proposed scheme in fusing multimodality medical images.

Fusion of multimodality Medical Images using combined Activity Level Measurement and Contourlet Transform

In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method, based on a novel combined Activity Level Measurement (ALM) and Contourlet Transform (CNT) for spatially registered, multi-sensor, multi-resolution medical images. The source medical images are first decomposed by CNT. The low-frequency subbands (LFSs) are fused using the novel combined ALM, and the high-frequency subbands (HFSs) are fused according to their ‘local average energy’ of the neighborhood of coefficients. Then inverse contourlet transform (ICNT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis and comparisons show the effectiveness of the proposed scheme in fusing multimodality medical images.

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.