Feature descriptor optimization in medical image retrieval based on Genetic Algorithm

Behnam, Mohammad; Pourghassem, Hossein

doi:10.1109/icbme.2013.6782235

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The use of evolutionary algorithms (EAs) in computer vision is not very common because, by their core nature, they take a long time to obtain the final results. References [16], [17] apply genetic algorithms (GAs) with Fourier descriptors for object recognition, where the GA serves as a query function for large image datasets. They report improved results compared to the original local features, but the recognition is a time-consuming process.…”

Section: A Related Workmentioning

confidence: 99%

“…Image datasets: (a)-(c) Oxford, (d) TILDE, (e) Brighton.transforming just one and keeping the other in the original format. UsingM = α β (1 − α) • center.x − β • center.y −β α β • center.x + (1 − α)• center.y where α = scale • cos angle β = scale • sin angle(17) for the rotation, in the resize operation, center refers to the rotation point around which the image is being turned, and angle is the rotation angle in degrees (positive values indicate a counterclockwise rotation). For the affine transformation (Fig.…”

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Descriptor Generation and Optimization for a Specific Outdoor Environment

et al. 2020

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Modern outdoor self-localizing computer vision applications require descriptors more than repeatability. The descriptors need to be invariant to light conditions and transformation changes to give support for efficient classification. This paper investigates a new framework based on the genetic algorithm to create and optimize extensible modular descriptors for specific outdoor environments. The algorithm returns descriptors with improved efficiency and classification performance. It controls the image processing and machine learning parameters and optimizes the descriptor size by activating the necessary modules. To show the strength of the descriptor, we compared it with the most commonly used standard descriptors on speed, accuracy, and invariance to light conditions, image resolution changes, scale, affine transformation, rotation, and classification. The results show that it has an average result in transformation invariance, and its description ability in sparse areas is significantly better than that of the most-used descriptors. The descriptor was also integrated into an augmented reality algorithm to create a self-regulating segmentation application.

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Section: A Related Workmentioning

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Descriptor Generation and Optimization for a Specific Outdoor Environment

et al. 2020

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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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A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence

Greeshma¹,

Gripsy²

2021

Internet of Things

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Feature descriptor optimization in medical image retrieval based on Genetic Algorithm

Cited by 5 publications

References 17 publications

Descriptor Generation and Optimization for a Specific Outdoor Environment

Descriptor Generation and Optimization for a Specific Outdoor Environment

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

A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence

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