Colour image annotation using hybrid intelligent techniques for image retrieval
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference proceedings
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- Description: This paper presents a novel technique for colour image annotation based on neural networks and fuzzy logic. Neural network is proposed for classifying the images based on their contents and fuzzy logic is proposed for interpreting the content of an image in terms of natural language. One of the main aspects of this research is to avoid re-training of the neural networks by training the content of the image. Neural network is not trained on database of images; therefore image can be added or deleted from image database without affecting the training. The proposed hybrid technique is tested on real world colour image dataset and promising results are obtained. © 2012 IEEE.
- Description: 2003010700
Texture feature extraction and classification by combining statistical and neural based technique for efficient CBIR
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 Int. Conf. on MulGraB 2012, the 2012 Int. Conf. on BSBT 2012, and the 1st Int. Conf. on Intelligent Urban Computing, IUrC 2012, Held as Part of the Future Generation Information Technology Conference, FGIT 2012 Vol. 353 CCIS, p. 106-113
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- Description: This paper presents a technique based on statistical and neural feature extractor, classifier and retrieval for real world texture images. The paper is presented into two stages, texture image pre-processing includes downloading images, normalizing into specific rows and columns, forming non-overlapping windows and extracting statistical features. Co-occrance based statistical technique is used for extracting four prominent texture features from an image. Stage two includes, feeding of these parameters to Multi-Layer Perceptron (MLP) as input and output. Hidden layer output was treated as characteristics of the patterns and fed to classifier to classify into six different classes. Graphical user interface was designed to pose a query of texture pattern and retrieval results are shown. © 2012 Springer-Verlag.
- Description: 2003010656
Visual character N-grams for classification and retrieval of radiological images
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
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- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
Pixel N-grams for mammographic lesion classification
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Ugon, Julien , Mittal, Manish , Kulkarni, Siddhivinayak
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA , Mumbai; 7th-8th April, 2017; published in CSCITA 2017 - Proceedings p. 107-111
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- Description: Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Framework for Integration of Medical Image and Text-Based Report Retrieval to Support Radiological Diagnosis
- Authors: Kulkarni, Siddhivinayak , Savyanavar, Amit , Kulkarni, Pradnya , Stranieri, Andrew , Ghorpade, Vijay
- Date: 2017
- Type: Text , Book chapter
- Relation: Biomedical Signal and Image Processing in Patient Care p. 86-122
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- Description: In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions. In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions.