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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Content-based image retrieval using features extracted from halftoning-based block truncation coding.

Jing-Ming Guo, Heri Prasetyo

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 25, 2014

Summary

This summary is machine-generated.

This study introduces a new content-based image retrieval (CBIR) method using ordered-dither block truncation coding (ODBTC). The ODBTC technique efficiently generates image descriptors for faster and more effective image indexing and retrieval.

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Area of Science:

Computer Science
Image Processing
Information Retrieval

Background:

Content-based image retrieval (CBIR) systems require efficient image descriptors.
Traditional methods can be computationally intensive.
Block truncation coding (BTC) offers a simpler approach but can be improved.

Purpose of the Study:

To develop a novel CBIR technique using ordered-dither block truncation coding (ODBTC).
To generate effective image content descriptors directly from ODBTC encoded data.
To evaluate the performance of the proposed ODBTC-based CBIR system.

Main Methods:

Utilizing low-complexity ordered-dither block truncation coding (ODBTC) for image compression and descriptor generation.
Generating color co-occurrence features (CCF) from ODBTC quantizers.
Generating bit pattern features (BPF) from ODBTC bitmap images.
Indexing images using CCF and BPF without decoding.

Main Results:

The proposed ODBTC-based CBIR method significantly outperforms existing block truncation coding (BTC) image retrieval systems.
Experimental results demonstrate superior performance compared to other earlier CBIR methods.
The ODBTC scheme proves effective for generating simple and efficient image descriptors.

Conclusions:

ODBTC is suitable not only for image compression due to its simplicity.
The ODBTC scheme provides a simple and effective descriptor for indexing images in CBIR systems.
This technique offers an efficient alternative for content-based image retrieval.