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Author

Manish Chowdhury

Other affiliations: Royal Institute of Technology
Bio: Manish Chowdhury is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Image retrieval & Contourlet. The author has an hindex of 11, co-authored 25 publications receiving 535 citations. Previous affiliations of Manish Chowdhury include Royal Institute of Technology.

Papers
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Journal ArticleDOI
TL;DR: Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods, which will be of particular help in early detection of cancer.

135 citations

Journal ArticleDOI
TL;DR: Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.
Abstract: 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.

110 citations

Proceedings ArticleDOI
18 Dec 2016
TL;DR: This article presents the result of a comprehensive study on deep learning based Computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images and shows that proposed system performs efficiently in Pap smear classification.
Abstract: This article presents the result of a comprehensive study on deep learning based Computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre-cancerous and cancerous lesion of cervix. The proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.

66 citations

Journal ArticleDOI
TL;DR: A novel CBIR scheme that abstracts each image in the database in terms of statistical features computed using the Multi-scale Geometric Analysis of Non-subsampled Contourlet Transform (NSCT) and incorporates a Relevance Feedback mechanism that uses a graph-theoretic approach to rank the images in accordance with the user's feedback.
Abstract: Content-Based Image Retrieval (CBIR) is an important problem in the domain of digital data management. There is indeed a growing availability of images, but unfortunately the traditional metadata-based search systems are unable to properly exploit their visual information content. In this article we introduce a novel CBIR scheme that abstracts each image in the database in terms of statistical features computed using the Multi-scale Geometric Analysis (MGA) of Non-subsampled Contourlet Transform (NSCT). Noise resilience is one of the main advantages of this feature representation. To improve the retrieval performance and reduce the semantic gap, our system incorporates a Relevance Feedback (RF) mechanism that uses a graph-theoretic approach to rank the images in accordance with the user's feedback. First, a graph of images is constructed with edges reflecting the similarity of pairs of images with respect to the proposed feature representation. Then, images are ranked at each feedback round in terms of the probability that a random walk on this graph reaches an image tagged as relevant by the user before hitting a non-relevant one. Experimental analyses on three different databases show the effectiveness of our algorithm compared to state-of-the-art approaches in particular when the images are corrupted with different types of noise.

63 citations

Journal ArticleDOI
TL;DR: Visual and quantitative analysis shows, that the proposed technique performs better compared to fusion scheme based on Contourlet Transform (CNT), as well as various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN).
Abstract: 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).

54 citations


Cited by
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01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations

Dissertation
01 Jan 2002

570 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented in this paper, where the challenges and potential of these techniques are also highlighted.
Abstract: The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

570 citations

Journal ArticleDOI
TL;DR: A novel fusion framework is proposed for multimodal medical images based on non-subsampled contourlet transform (NSCT) to enable more accurate analysis of multimodality images.
Abstract: Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on non-subsampled contourlet transform (NSCT). The source medical images are first transformed by NSCT followed by combining low- and high-frequency components. Two different fusion rules based on phase congruency and directive contrast are proposed and used to fuse low- and high-frequency coefficients. Finally, the fused image is constructed by the inverse NSCT with all composite coefficients. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images. Further, the applicability of the proposed framework is carried out by the three clinical examples of persons affected with Alzheimer, subacute stroke and recurrent tumor.

381 citations

Journal ArticleDOI
TL;DR: A generic computer vision system designed for exploiting trained deep Convolutional Neural Networks as a generic feature extractor and mixing these features with more traditional hand-crafted features is presented, demonstrating the generalizability of the proposed approach.

376 citations