Improving the ranking quality of medical image retrieval using a genetic feature selection method

Silva, Sérgio Francisco da; Ribeiro, Marcela Xavier; Neto, João Dias de Toledo Arruda; Traina-Jr, Caetano; Traina, Agma J. M.

doi:10.1016/j.dss.2011.01.015

Cited by 98 publications

(53 citation statements)

References 27 publications

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“…Diverse techniques have been proposed for evaluating the support of feature subsets in classification problems, including genetic algorithms [5], statistical methods based on mutual information [6] and fuzzy measures [7,8,9]. A main drawback of genetic approaches is that they cannot explain the selection criteria; this drawback is partially solved by mutual information approaches able to explain the selection of feature pairs.…”

Section: Introductionmentioning

confidence: 99%

Set characterization-selection towards classification based on interaction index

Murillo

Guillaume²,

Spetale

et al. 2015

Fuzzy Sets and Systems

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In many real world datasets both the individual and coordinated action of features may be relevant for class identification. In this paper, a computational strategy for relevant feature selection based on the characterization of redundant or complementary features is proposed. The characterization is achieved using fuzzy measures and an interaction index computed from fuzzy measure coefficients. Fuzzy measure identification requires raw data to be turned into confidence degrees. This key step is carried out considering the distributions of feature values across all the classes. Fuzzy measure coefficients are then estimated with an improved version of the Heuristic Least Mean Squares algorithm that includes an efficient management of untouched coefficients. Then, a generalization of the Shapley index for an arbitrary number of features is used. Simulations experiments on synthetic datasets are performed to study the behavior of this generalized interaction index. For extreme datasets, containing either redundant or complementary features as well as noise, the index value is defined by mathematical formula. This result is used to motivate feature selection guidelines that take into account feature interactions. Experimental results on benchmark datasets show that the proposal allows for the design of compact, interpretable and competitive classification models.

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Section: Introductionmentioning

confidence: 99%

Set characterization-selection towards classification based on interaction index

Murillo

Guillaume²,

Spetale

et al. 2015

Fuzzy Sets and Systems

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“…Accordingly, the optimal queries are found by applying a simple scenario on the optimized relevant points. However, in the image retrieval domain, several techniques based on GA have been presented in the literature, but these papers have mostly focused on feature selection and dimensionality reduction [26,27]. In the study of Steji´c et al [28], a GA-based RF has been presented to infer the (sub-)optimal assignment of region and feature weights, which maximizes the similarity between the query image and the set of relevant images.…”

Section: Introductionmentioning

confidence: 99%

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Behnam

Pourghassem

2014

J Digit Imaging

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In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

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“…Other works attempt to improve precision of CBIR systems by working directly with the feature space [13] [5]. The work of Silva et al [5] relies on ranking evaluation functions in order to choose the best set of features to represent images in the CBIR context.…”

Section: Related Workmentioning

confidence: 99%

Image retrieval employing genetic dissimilarity weighting and feature space transformation functions

Avalhais

Silva

Rodrigues

et al. 2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the performance on the task of image retrieval according to the user's interests. The first method adjusts the dissimilarity function by using weighting functions while the second method redefines the feature space by means of linear and nonlinear transformation functions. Experimental results on real datasets demonstrate that our methods are effective and the results show that the transformation approach outperforms the weighting approach, achieving a precision gain of up to 70%.

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Improving the ranking quality of medical image retrieval using a genetic feature selection method

Cited by 98 publications

References 27 publications

Set characterization-selection towards classification based on interaction index

Set characterization-selection towards classification based on interaction index

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Image retrieval employing genetic dissimilarity weighting and feature space transformation functions

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