Content-based retrieval of compressed medical images

Schaefer, Gerald; Starosolski, Roman; Moleda, Marek; Zhu, Shao Ying

doi:10.1109/itab.2008.4570597

Cited by 2 publications

(2 citation statements)

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“…While humans can match similar images or objects, machine vision research is still away from similar performance for computers. Currently, many retrieval approaches are based on low-level features such as color, texture, and shape features, leaving a 'semantic gap' to the high-level understanding of users (Schaefer 2010).…”

Section: Introductionmentioning

confidence: 99%

Improved cuckoo search with particle swarm optimization for classification of compressed images

Enireddy

Kumar

2015

Sadhana

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The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO)-Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.

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Section: Introductionmentioning

confidence: 99%

Improved cuckoo search with particle swarm optimization for classification of compressed images

Enireddy

Kumar

2015

Sadhana

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“…While CBIR feature extraction needs to be performed quickly, especially for large image datasets, this is hindered by relatively slow image decompression that needs to be employed before features can be extracted from the pixel domain. A faster approach is to perform this directly in the compressed domain, that is to perform compressed domain image retrieval (Mandal, Idris, & Panchanathan, 1999;Schaefer, 2010) and support so-called mid-stream content access (Picard, 1994).…”

Section: Introductionmentioning

confidence: 99%

Jirl

Edmundson

Schaefer

2013

International Journal of Multimedia Data Engineering and Management

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Since there are few open image retrieval toolkits available, researchers in the field are often forced to re-implement existing algorithms in order to perform a comparative evaluation. None of the existing toolkits support retrieval of JPEG images directly in the compressed domain. The authors’ aim is therefore to facilitate the use of compressed domain image retrieval techniques as well as ease retrieval evaluation by fellow researchers. For this purpose, the authors present JIRL, an open source C++ software suite that allows content-based image retrieval in the JPEG compressed domain and provides tools for benchmarking retrieval accuracy and retrieval time. In total, twelve state-of-the-art JPEG retrieval algorithms are implemented, while for each method techniques for compressed domain feature extraction as well as feature comparison are provided in an object-oriented framework. An example image retrieval application is also provided to demonstrate how the library can be used. JIRL is made available to fellow researchers under the LGPL v.2.1 license.

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Content-based retrieval of compressed medical images

Cited by 2 publications

References 10 publications

Improved cuckoo search with particle swarm optimization for classification of compressed images

Improved cuckoo search with particle swarm optimization for classification of compressed images

Jirl

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