2010
1.
Rahman, Md Mahmudur; Antani, Sameer K; Thoma, George R
A Classification-Driven Similarity Matching Framework for Retrieval of Biomedical Images Proceedings Article
In: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 147–154, Association for Computing Machinery, Philadelphia, Pennsylvania, USA, 2010, ISBN: 9781605588155.
Abstract | Links | BibTeX | Tags: Classification, Classifier combination, Content-based image retrieval, Medical imaging, similarity matching, Support vector machine
@inproceedings{10.1145/1743384.1743413,
title = {A Classification-Driven Similarity Matching Framework for Retrieval of Biomedical Images},
author = {Md Mahmudur Rahman and Sameer K Antani and George R Thoma},
url = {https://doi.org/10.1145/1743384.1743413},
doi = {10.1145/1743384.1743413},
isbn = {9781605588155},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of the International Conference on Multimedia Information Retrieval},
pages = {147–154},
publisher = {Association for Computing Machinery},
address = {Philadelphia, Pennsylvania, USA},
series = {MIR '10},
abstract = {This paper presents a classification-driven biomedical image retrieval system to bride
the semantic gap by transforming image features to their global categories at different
granularity, such as image modality, body part, and orientation. To generate the feature
vectors at different levels of abstraction, both the visual concept feature based
on the "bag of concepts" model that comprise of local color and texture patches and
various low-level global color, edge, and texture-related features are extracted.
Since, it is difficult to find a unique feature to compare images effectively for
all types of queries, we utilize a similarity fusion approach based on the linear
combination of individual features. However, instead of using the commonly used fixed
or hard weighting approach, we rely on the image classification to determine the importance
of a feature at real time. For this, a supervised multi-class classifier based on
the support vector machine (SVM) is trained on a set of sample images and classifier
combination techniques based on the rules derived from the Bayes's theorem are explored.
After the combined prediction of the classifiers for a query image category, the individual
pre-computed weights of different features are adjusted in the similarity matching
function for effective query-specific retrieval. Experiment is performed in a diverse
medical image collection of 67,000 images of different modalities. It demonstrates
the effectiveness of the category-specific similarity fusion approach with a mean
average precision (MAP) score of 0.0265 when compared to using only a single feature
or equal weighting of each feature in similarity matching.},
keywords = {Classification, Classifier combination, Content-based image retrieval, Medical imaging, similarity matching, Support vector machine},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a classification-driven biomedical image retrieval system to bride
the semantic gap by transforming image features to their global categories at different
granularity, such as image modality, body part, and orientation. To generate the feature
vectors at different levels of abstraction, both the visual concept feature based
on the "bag of concepts" model that comprise of local color and texture patches and
various low-level global color, edge, and texture-related features are extracted.
Since, it is difficult to find a unique feature to compare images effectively for
all types of queries, we utilize a similarity fusion approach based on the linear
combination of individual features. However, instead of using the commonly used fixed
or hard weighting approach, we rely on the image classification to determine the importance
of a feature at real time. For this, a supervised multi-class classifier based on
the support vector machine (SVM) is trained on a set of sample images and classifier
combination techniques based on the rules derived from the Bayes's theorem are explored.
After the combined prediction of the classifiers for a query image category, the individual
pre-computed weights of different features are adjusted in the similarity matching
function for effective query-specific retrieval. Experiment is performed in a diverse
medical image collection of 67,000 images of different modalities. It demonstrates
the effectiveness of the category-specific similarity fusion approach with a mean
average precision (MAP) score of 0.0265 when compared to using only a single feature
or equal weighting of each feature in similarity matching.
the semantic gap by transforming image features to their global categories at different
granularity, such as image modality, body part, and orientation. To generate the feature
vectors at different levels of abstraction, both the visual concept feature based
on the "bag of concepts" model that comprise of local color and texture patches and
various low-level global color, edge, and texture-related features are extracted.
Since, it is difficult to find a unique feature to compare images effectively for
all types of queries, we utilize a similarity fusion approach based on the linear
combination of individual features. However, instead of using the commonly used fixed
or hard weighting approach, we rely on the image classification to determine the importance
of a feature at real time. For this, a supervised multi-class classifier based on
the support vector machine (SVM) is trained on a set of sample images and classifier
combination techniques based on the rules derived from the Bayes's theorem are explored.
After the combined prediction of the classifiers for a query image category, the individual
pre-computed weights of different features are adjusted in the similarity matching
function for effective query-specific retrieval. Experiment is performed in a diverse
medical image collection of 67,000 images of different modalities. It demonstrates
the effectiveness of the category-specific similarity fusion approach with a mean
average precision (MAP) score of 0.0265 when compared to using only a single feature
or equal weighting of each feature in similarity matching.
2008
2.
Rahman, Md. Mahmudur; Desai, Bipin C; Bhattacharya, Prabir
Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion Journal Article
In: Computerized Medical Imaging and Graphics, vol. 32, no. 2, pp. 95-108, 2008, ISSN: 0895-6111.
Abstract | Links | BibTeX | Tags: Classification, Classifier combination, Content-based image retrieval, Inverted file, Medical imaging, Similarity fusion, Support vector machine
@article{RAHMAN200895,
title = {Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion},
author = {Md. Mahmudur Rahman and Bipin C Desai and Prabir Bhattacharya},
url = {https://www.sciencedirect.com/science/article/pii/S0895611107001383},
doi = {https://doi.org/10.1016/j.compmedimag.2007.10.001},
issn = {0895-6111},
year = {2008},
date = {2008-01-01},
journal = {Computerized Medical Imaging and Graphics},
volume = {32},
number = {2},
pages = {95-108},
abstract = {We present a content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems. For the image representation, the probabilistic output from multi-class support vector machines (SVMs) with low-level features as inputs are represented as a vector of confidence or membership scores of pre-defined image categories. The outputs are combined for feature-level fusion and retrieval based on the combination rules that are derived by following Bayes’ theorem. We also propose an adaptive similarity fusion approach based on a linear combination of individual feature level similarities. The feature weights are calculated by considering both the precision and the rank order information of top retrieved relevant images as predicted by SVMs. The weights are dynamically updated by the system for each individual search to produce effective results. The experiments and analysis of the results are based on a diverse medical image collection of 11,000 images of 116 categories. The performances of the classification and retrieval algorithms are evaluated both in terms of error rate and precision–recall. Our results demonstrate the effectiveness of the proposed framework as compared to the commonly used approaches based on low-level feature descriptors.},
keywords = {Classification, Classifier combination, Content-based image retrieval, Inverted file, Medical imaging, Similarity fusion, Support vector machine},
pubstate = {published},
tppubtype = {article}
}
We present a content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems. For the image representation, the probabilistic output from multi-class support vector machines (SVMs) with low-level features as inputs are represented as a vector of confidence or membership scores of pre-defined image categories. The outputs are combined for feature-level fusion and retrieval based on the combination rules that are derived by following Bayes’ theorem. We also propose an adaptive similarity fusion approach based on a linear combination of individual feature level similarities. The feature weights are calculated by considering both the precision and the rank order information of top retrieved relevant images as predicted by SVMs. The weights are dynamically updated by the system for each individual search to produce effective results. The experiments and analysis of the results are based on a diverse medical image collection of 11,000 images of 116 categories. The performances of the classification and retrieval algorithms are evaluated both in terms of error rate and precision–recall. Our results demonstrate the effectiveness of the proposed framework as compared to the commonly used approaches based on low-level feature descriptors.